Patent Publication Number: US-2022237189-A1

Title: Building management system with eventseries processing

Description:
CROSS-REFERENCE TO RELATED PATENT APPLICATION 
     This application is a continuation of U.S. patent application Ser. No. 16/520,865 filed Jul. 24, 2019 which is a continuation of U.S. patent application Ser. No. 15/644,560 filed Jul. 7, 2017, which claims the benefit of and priority to U.S. Provisional Patent Application No. 62/457,654 filed Feb. 10, 2017, the entire disclosure of each of these patent applications is incorporated by reference herein. 
    
    
     BACKGROUND 
     The present disclosure relates generally to the field of building management systems. A building management system (BMS) is, in general, a system of devices configured to control, monitor, and manage equipment in or around a building or building area. A BMS can include, for example, a HVAC system, a security system, a lighting system, a fire alerting system, any other system that is capable of managing building functions or devices, or any combination thereof. 
     A BMS can collect data from sensors and other types of building equipment. Data can be collected over time and combined into streams of timeseries data. Each sample of the timeseries data can include a timestamp and a data value. Some BMSs store raw timeseries data in a relational database without significant organization or processing at the time of data collection. Applications that consume the timeseries data are typically responsible for retrieving the raw timeseries data from the database and generating views of the timeseries data that can be presented via a chart, graph, or other user interface. These processing steps are typically performed in response to a request for the timeseries data, which can significantly delay data presentation at query time. 
     SUMMARY 
     One implementation of the present disclosure is a building management system (BMS). The BMS includes building equipment, an eventseries generator, and a storage interface. The building equipment are configured to generate a plurality of data samples. Each of the data samples includes a data value and a timestamp. The eventseries generator is configured to assign a sample state to each data sample based on the data value of the data sample and generate one or more events based on the timestamp of each data sample and the sample state assigned to each data sample. Each of the events includes a start time, an end time, and an event state. The eventseries generator is configured to generate an eventseries including the one or more generated events. The storage interface is configured to store the eventseries in an eventseries database. 
     In some embodiments, the eventseries generator is configured to assign the sample state by applying a set of rules to the data sample. The set of rules may define a plurality of sample states and may include criteria for assigning each of the plurality of sample states to the data sample. 
     In some embodiments, the eventseries generator is configured to identify a timeseries corresponding to the data sample, select a rule to apply to the data sample based on the identified timeseries, and evaluate one or more conditions of the selected rule. Evaluating at least one of the conditions may include comparing the data value of the data sample to a threshold value. The eventseries generator can assign the sample state to the data sample based on which of the one or more conditions are satisfied 
     In some embodiments, each event defines an event period between the start time of the event and the end time of the event. The eventseries generator may be configured to set the start time of the event and the end time of the event such that the event period includes the timestamps of one or more consecutive data samples assigned the same sample state. 
     In some embodiments, the eventseries generator is configured to identify a group of the data samples that have consecutive timestamps and the same sample state, generate an event for the identified group of the data samples, and set the start time of the event and the end time of the event such that each of the consecutive timestamps is between the start time of the event and the end time of the event. 
     In some embodiments, the eventseries generator is configured to receive a new data sample, assign a sample state to the new data sample, and determine whether the new data sample is part of an existing event in the eventseries. The new data sample may be part of the existing event if both (1) the sample state of the new data sample is the same as the event state of the existing event and (2) a timestamp of the new data sample is either (a) within an event period between the start time and the end time of the existing event or (b) consecutive with the event period. 
     In some embodiments, the eventseries generator is configured to receive a new data sample, assign a sample state to the new data sample, and determine that the new data sample is part of an existing event in the eventseries. The existing event may have an event state corresponding to the sample state of the new data sample. The eventseries generator can be configured to update at least one of the start time of the existing event or the end time of the existing event based on a timestamp of the new data sample. 
     In some embodiments, the eventseries generator is configured to receive a new data sample, assign a sample state to the new data sample, determine that the new data sample is not part of any existing event in the eventseries, and add a new event to the eventseries. The new event may have a start time based on a timestamp of the new data sample and an event state corresponding to the sample state of the new data sample. 
     In some embodiments, the eventseries generator is configured to identify an existing event in the eventseries temporally adjacent to the new event and update at least one of the start time of the existing event or the end time of the existing event based on the timestamp of the new data sample. 
     In some embodiments, the eventseries generator is configured to determine that the new event has a different event state from an existing event in the eventseries but occurs between the start time of the existing event and the end time of the existing event. The eventseries generator can be configured to split the existing event into two non-consecutive events having the same event state as the existing event. A first of the non-consecutive events may occur before the new event, whereas a second of the non-consecutive events may occur after the new event. 
     Another implementation of the present disclosure is a method for generating eventseries in a building management system. The method includes operating building equipment to generate a plurality of data samples. Each of the data samples includes a data value and a timestamp. The method includes assigning a sample state to each data sample based on the data value of the data sample and generating one or more events based on the timestamp of each data sample and the sample state assigned to each data sample. Each of the events includes a start time, an end time, and an event state. The method includes generating an eventseries including the one or more generated events and storing the eventseries in an eventseries database. 
     In some embodiments, assigning the sample state includes applying a set of rules to the data sample. The set of rules may define a plurality of sample states and may include criteria for assigning each of the plurality of sample states to the data sample. 
     In some embodiments, assigning the sample state includes identifying a timeseries corresponding to the data sample, select a rule to apply to the data sample based on the identified timeseries, and evaluate one or more conditions of the selected rule. Evaluating at least one of the conditions may include comparing the data value of the data sample to a threshold value. The method may include assigning the sample state to the data sample based on which of the one or more conditions are satisfied. 
     In some embodiments, each event defines an event period between the start time of the event and the end time of the event. The method may include setting the start time of the event and the end time of the event such that the event period includes the timestamps of one or more consecutive data samples assigned the same sample state. 
     In some embodiments, generating the one or more events includes identifying a group of the data samples that have consecutive timestamps and the same sample state, generating an event for the identified group of the data samples, and setting the start time of the event and the end time of the event such that each of the consecutive timestamps is between the start time of the event and the end time of the event. 
     In some embodiments, the method includes receiving a new data sample, assigning a sample state to the new data sample, and determining whether the new data sample is part of an existing event in the eventseries. The new data sample may be part of the existing event if both (1) the sample state of the new data sample is the same as the event state of the existing event and (2) a timestamp of the new data sample is either (a) within an event period between the start time and the end time of the existing event or (b) consecutive with the event period. 
     In some embodiments, the method includes receiving a new data sample, assigning a sample state to the new data sample, and determining that the new data sample is part of an existing event in the eventseries. The existing event may have an event state corresponding to the sample state of the new data sample. The method may include updating at least one of the start time of the existing event or the end time of the existing event based on a timestamp of the new data sample. 
     In some embodiments, the method includes receiving a new data sample, assigning a sample state to the new data sample, determining that the new data sample is not part of any existing event in the eventseries, and adding a new event to the eventseries. The new event may have a start time based on a timestamp of the new data sample and an event state corresponding to the sample state of the new data sample. 
     In some embodiments, the method includes identifying an existing event in the eventseries temporally adjacent to the new event and updating at least one of the start time of the existing event or the end time of the existing event based on the timestamp of the new data sample. 
     In some embodiments, the method includes determining that the new event has a different event state from an existing event in the eventseries but occurs between the start time of the existing event and the end time of the existing event. The method may include splitting the existing event into two non-consecutive events having the same event state as the existing event. A first of the non-consecutive events may occur before the new event, whereas a second of the non-consecutive events may occur after the new event. 
     Those skilled in the art will appreciate that the summary is illustrative only and is not intended to be in any way limiting. Other aspects, inventive features, and advantages of the devices and/or processes described herein, as defined solely by the claims, will become apparent in the detailed description set forth herein and taken in conjunction with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a drawing of a building equipped with a building management system (BMS) and a HVAC system, according to some embodiments. 
         FIG. 2  is a schematic of a waterside system which can be used as part of the HVAC system of  FIG. 1 , according to some embodiments. 
         FIG. 3  is a block diagram of an airside system which can be used as part of the HVAC system of  FIG. 1 , according to some embodiments. 
         FIG. 4  is a block diagram of a BMS which can be used in the building of  FIG. 1 , according to some embodiments. 
         FIG. 5  is a block diagram of another BMS which can be used in the building of  FIG. 1 , including a data collector, data platform services, applications, and a dashboard layout generator, according to some embodiments. 
         FIG. 6  is a block diagram of a timeseries service which can be implemented as some of the data platform services shown in  FIG. 5 , according to some embodiments. 
         FIG. 7A  is a block diagram illustrating an aggregation technique which can be used by the sample aggregator shown in  FIG. 6  to aggregate raw data samples, according to some embodiments. 
         FIG. 7B  is a data table which can be used to store raw data timeseries and a variety of derived data timeseries which can be generated by the timeseries service of  FIG. 6 , according to some embodiments. 
         FIG. 8  is a drawing of several timeseries illustrating the synchronization of data samples which can be performed by the data aggregator shown in  FIG. 6 , according to some embodiments. 
         FIG. 9A  is a flow diagram illustrating the creation and storage of a fault detection timeseries which can be performed by the fault detector shown in  FIG. 6 , according to some embodiments. 
         FIG. 9B  is a data table which can be used to store the raw data timeseries and the fault detection timeseries, according to some embodiments. 
         FIG. 9C  is a data table which can be used to store states assigned to samples of a data timeseries, according to some embodiments. 
         FIG. 9D  is a data table including various events generated based on the assigned states shown in the table of  FIG. 9C , according to some embodiments. 
         FIG. 9E  is a data table including a timeseries of data values and assigned states, according to some embodiments. 
         FIG. 9F  is a data table including events which can be generated based on a first portion of the data table of  FIG. 9E , according to some embodiments. 
         FIG. 9G  is a data table illustrating updates to the events shown in the data table of  FIG. 9F  which can be made upon receiving a new sample of the timeseries shown in  FIG. 9E , according to some embodiments. 
         FIG. 9H  is another data table illustrating updates to the events shown in the data table of  FIG. 9G  which can be made upon receiving a new sample of the timeseries shown in  FIG. 9E , according to some embodiments. 
         FIG. 9I  is a data table including a timeseries of data values and assigned states in which one of the data samples is received out of order, according to some embodiments. 
         FIG. 9J  is a data table including events which can be generated based on a first portion of the data table of  FIG. 9I , according to some embodiments. 
         FIG. 9K  is a data table illustrating updates to the events shown in the data table of  FIG. 9J  which can be made upon receiving a new sample of the timeseries shown in  FIG. 9I , according to some embodiments. 
         FIG. 9L  is another data table illustrating updates to the events shown in the data table of  FIG. 9K  which can be made upon receiving a new sample of the timeseries shown in  FIG. 9I , according to some embodiments. 
         FIG. 9M  is another data table illustrating updates to the events shown in the data table of  FIG. 9L  which can be made upon receiving a new sample of the timeseries shown in  FIG. 9I , according to some embodiments. 
         FIG. 9N  is another data table including a timeseries of data values and assigned states in which one of the data samples is received out of order, according to some embodiments. 
         FIG. 9O  is a data table including events which can be generated based on a first portion of the data table of  FIG. 9N , according to some embodiments. 
         FIG. 9P  is a data table illustrating updates to the events shown in the data table of  FIG. 9O  which can be made upon receiving a new sample of the timeseries shown in  FIG. 9N , according to some embodiments. 
         FIG. 9Q  is another data table illustrating updates to the events shown in the data table of  FIG. 9P  which can be made upon receiving a new sample of the timeseries shown in  FIG. 9N , according to some embodiments. 
         FIG. 9R  is another data table illustrating updates to the events shown in the data table of  FIG. 9P  which can be made upon receiving a new sample of the timeseries shown in  FIG. 9N , according to some embodiments. 
         FIG. 9S  is a data table including a timeseries of data values and assigned states in which several of the data samples are received out of order, according to some embodiments. 
         FIG. 9T  is a data table including events which can be generated based on a first portion of the data table of  FIG. 9S , according to some embodiments. 
         FIG. 9U  is a data table illustrating updates to the events shown in the data table of  FIG. 9T  which can be made upon receiving a new sample of the timeseries shown in  FIG. 9S , according to some embodiments. 
         FIG. 9V  is another data table illustrating updates to the events shown in the data table of  FIG. 9U  which can be made upon receiving a new sample of the timeseries shown in  FIG. 9S , according to some embodiments. 
         FIG. 9W  is another data table including a timeseries of data values and assigned states in which several of the data samples are received out of order, according to some embodiments. 
         FIG. 9X  is a data table illustrating updates to the events shown in the data table of  FIG. 9V  which can be made upon receiving a new sample of the timeseries shown in  FIG. 9W , according to some embodiments. 
         FIG. 9Y  is another data table illustrating updates to the events shown in the data table of  FIG. 9X  which can be made upon receiving a new sample of the timeseries shown in  FIG. 9W , according to some embodiments. 
         FIG. 9Z  is a flowchart of a process for generating and updating events and eventseries, according to some embodiments. 
         FIG. 10A  is a directed acyclic graph (DAG) which can be generated by the DAG generator of  FIG. 6 , according to some embodiments. 
         FIG. 10B  is a code snippet which can be automatically generated by the DAG generator of  FIG. 6  based on the DAG, according to some embodiments. 
         FIG. 11A  is an entity graph illustrating relationships between an organization, a space, a system, a point, and a timeseries, which can be used by the data collector of  FIG. 5 , according to some embodiments. 
         FIG. 11B  is an example of an entity graph for a particular building management system according to some embodiments. 
         FIG. 12  is an object relationship diagram illustrating relationships between an entity template, a point, a timeseries, and a data sample, which can be used by the data collector of  FIG. 5  and the timeseries service of  FIG. 6 , according to some embodiments. 
         FIG. 13A  is a block diagram illustrating a timeseries processing workflow which can be performed by the timeseries service of  FIGS. 5-6 , according to some embodiments. 
         FIG. 13B  is a flowchart of a process which can be performed by the workflow manager of  FIG. 13A , according to some embodiments. 
         FIG. 14  is a block diagram illustrating a silo configured IoT environment  1400 , according to some embodiments. 
         FIG. 15  is a block diagram illustrating a decentralized IoT environment, according to some embodiments. 
         FIG. 16  is a block diagram illustrating a multi-modal data processing service, according to some embodiments. 
         FIG. 17  is an example user interface providing a view of multi-modal data, according to some embodiments. 
         FIG. 18  is a block diagram illustrating an IoT application storage topology, according to some embodiments. 
         FIG. 19  is a block diagram illustrating a data scheme associated with a piece of equipment in a BMS, according to some embodiments. 
         FIG. 20  is a data map illustrating data mapping between entity/document stores and streamed data (e.g. telemetry data) stores, according to some embodiments. 
         FIG. 21  is a block diagram illustrating a reference abstraction architecture, according to some embodiments. 
         FIG. 22  is a flow chart illustrating a process for performing unified stream processing, according to some embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Overview 
     Referring generally to the FIGURES, a building management system (BMS) with declarative views of timeseries data is shown, according to various embodiments. The BMS is configured to collect data samples from building equipment (e.g., sensors, controllable devices, building subsystems, etc.) and generate raw timeseries data from the data samples. The BMS can process the raw timeseries data using a variety of data platform services to generate derived timeseries data (e.g., data rollup timeseries, virtual point timeseries, fault detection timeseries, etc.). The derived timeseries data can be provided to various applications and/or stored in local or hosted storage. In some embodiments, the BMS includes three different layers that separate (1) data collection, (2) data storage, retrieval, and analysis, and (3) data visualization. This allows the BMS to support a variety of applications that use the derived timeseries data and allows new applications to reuse the infrastructure provided by the data platform services. 
     In some embodiments, the BMS includes a data collector configured to collect raw data samples from the building equipment. The data collector can generate a raw data timeseries including a plurality of the raw data samples and store the raw data timeseries in the timeseries database. In some embodiments, the data collector stores each of the raw data samples with a timestamp. The timestamp can include a local time indicating the time at which the raw data sample was collected in whichever time zone the raw data sample was collected. The timestamp can also include a time offset indicating a difference between the local time and universal time. The combination of the local timestamp and the offset provides a unique timestamp across daylight saving time boundaries. This allows an application using the timeseries data to display the timeseries data in local time without first converting from universal time. The combination of the local timestamp and the offset also provides enough information to convert the local timestamp to universal time without needing to look up a schedule of when daylight savings time occurs. 
     In some embodiments, the data platform services include a sample aggregator. The sample aggregator can aggregate predefined intervals of the raw timeseries data (e.g., quarter-hourly intervals, hourly intervals, daily intervals, monthly intervals, etc.) to generate new derived timeseries of the aggregated values. These derived timeseries can be referred to as “data rollups” since they are condensed versions of the raw timeseries data. The data rollups generated by the data aggregator provide an efficient mechanism for various applications to query the timeseries data. For example, the applications can construct visualizations of the timeseries data (e.g., charts, graphs, etc.) using the pre-aggregated data rollups instead of the raw timeseries data. This allows the applications to simply retrieve and present the pre-aggregated data rollups without requiring applications to perform an aggregation in response to the query. Since the data rollups are pre-aggregated, the applications can present the data rollups quickly and efficiently without requiring additional processing at query time to generate aggregated timeseries values. 
     In some embodiments, the data platform services include a virtual point calculator. The virtual point calculator can calculate virtual points based on the raw timeseries data and/or the derived timeseries data. Virtual points can be calculated by applying any of a variety of mathematical operations (e.g., addition, subtraction, multiplication, division, etc.) or functions (e.g., average value, maximum value, minimum value, thermodynamic functions, linear functions, nonlinear functions, etc.) to the actual data points represented by the timeseries data. For example, the virtual point calculator can calculate a virtual data point (pointID 3 ) by adding two or more actual data points (pointID 1  and pointID 2 ) (e.g., pointID 3 =pointID 1 +pointID 2 ). As another example, the virtual point calculator can calculate an enthalpy data point (pointID 4 ) based on a measured temperature data point (pointID 5 ) and a measured pressure data point (pointID 6 ) (e.g., pointID 4 =enthalpy(pointID 5 , pointID 6 )). The virtual data points can be stored as derived timeseries data. 
     Applications can access and use the virtual data points in the same manner as the actual data points. The applications do not need to know whether a data point is an actual data point or a virtual data point since both types of data points can be stored as derived timeseries data and can be handled in the same manner by the applications. In some embodiments, the derived timeseries data are stored with attributes designating each data point as either a virtual data point or an actual data point. Such attributes allow the applications to identify whether a given timeseries represents a virtual data point or an actual data point, even though both types of data points can be handled in the same manner by the applications. 
     In some embodiments, the data platform services include a fault detector configured to analyze the timeseries data to detect faults. Fault detection can be performed by applying a set of fault detection rules to the timeseries data to determine whether a fault is detected at each interval of the timeseries. Fault detections can be stored as derived timeseries data. For example, new timeseries can be generated with data values that indicate whether a fault was detected at each interval of the timeseries. The time series of fault detections can be stored along with the raw timeseries data and/or derived timeseries data in local or hosted data storage. These and other features of the building management system are described in greater detail below. 
     Building Management System and HVAC System 
     Referring now to  FIGS. 1-4 , an exemplary building management system (BMS) and HVAC system in which the systems and methods of the present disclosure can be implemented are shown, according to an exemplary embodiment. Referring particularly to  FIG. 1 , a perspective view of a building  10  is shown. Building  10  is served by a BMS. A BMS is, in general, a system of devices configured to control, monitor, and manage equipment in or around a building or building area. A BMS can include, for example, a HVAC system, a security system, a lighting system, a fire alerting system, any other system that is capable of managing building functions or devices, or any combination thereof. 
     The BMS that serves building  10  includes an HVAC system  100 . HVAC system  100  can include a plurality of HVAC devices (e.g., heaters, chillers, air handling units, pumps, fans, thermal energy storage, etc.) configured to provide heating, cooling, ventilation, or other services for building  10 . For example, HVAC system  100  is shown to include a waterside system  120  and an airside system  130 . Waterside system  120  can provide a heated or chilled fluid to an air handling unit of airside system  130 . Airside system  130  can use the heated or chilled fluid to heat or cool an airflow provided to building  10 . An exemplary waterside system and airside system which can be used in HVAC system  100  are described in greater detail with reference to  FIGS. 2-3 . 
     HVAC system  100  is shown to include a chiller  102 , a boiler  104 , and a rooftop air handling unit (AHU)  106 . Waterside system  120  can use boiler  104  and chiller  102  to heat or cool a working fluid (e.g., water, glycol, etc.) and can circulate the working fluid to AHU  106 . In various embodiments, the HVAC devices of waterside system  120  can be located in or around building  10  (as shown in  FIG. 1 ) or at an offsite location such as a central plant (e.g., a chiller plant, a steam plant, a heat plant, etc.). The working fluid can be heated in boiler  104  or cooled in chiller  102 , depending on whether heating or cooling is required in building  10 . Boiler  104  can add heat to the circulated fluid, for example, by burning a combustible material (e.g., natural gas) or using an electric heating element. Chiller  102  can place the circulated fluid in a heat exchange relationship with another fluid (e.g., a refrigerant) in a heat exchanger (e.g., an evaporator) to absorb heat from the circulated fluid. The working fluid from chiller  102  and/or boiler  104  can be transported to AHU  106  via piping  108 . 
     AHU  106  can place the working fluid in a heat exchange relationship with an airflow passing through AHU  106  (e.g., via one or more stages of cooling coils and/or heating coils). The airflow can be, for example, outside air, return air from within building  10 , or a combination of both. AHU  106  can transfer heat between the airflow and the working fluid to provide heating or cooling for the airflow. For example, AHU  106  can include one or more fans or blowers configured to pass the airflow over or through a heat exchanger containing the working fluid. The working fluid can then return to chiller  102  or boiler  104  via piping  110 . 
     Airside system  130  can deliver the airflow supplied by AHU  106  (i.e., the supply airflow) to building  10  via air supply ducts  112  and can provide return air from building  10  to AHU  106  via air return ducts  114 . In some embodiments, airside system  130  includes multiple variable air volume (VAV) units  116 . For example, airside system  130  is shown to include a separate VAV unit  116  on each floor or zone of building  10 . VAV units  116  can include dampers or other flow control elements that can be operated to control an amount of the supply airflow provided to individual zones of building  10 . In other embodiments, airside system  130  delivers the supply airflow into one or more zones of building  10  (e.g., via supply ducts  112 ) without using intermediate VAV units  116  or other flow control elements. AHU  106  can include various sensors (e.g., temperature sensors, pressure sensors, etc.) configured to measure attributes of the supply airflow. AHU  106  can receive input from sensors located within AHU  106  and/or within the building zone and can adjust the flow rate, temperature, or other attributes of the supply airflow through AHU  106  to achieve setpoint conditions for the building zone. 
     Referring now to  FIG. 2 , a block diagram of a waterside system  200  is shown, according to an exemplary embodiment. In various embodiments, waterside system  200  can supplement or replace waterside system  120  in HVAC system  100  or can be implemented separate from HVAC system  100 . When implemented in HVAC system  100 , waterside system  200  can include a subset of the HVAC devices in HVAC system  100  (e.g., boiler  104 , chiller  102 , pumps, valves, etc.) and can operate to supply a heated or chilled fluid to AHU  106 . The HVAC devices of waterside system  200  can be located within building  10  (e.g., as components of waterside system  120 ) or at an offsite location such as a central plant. 
     In  FIG. 2 , waterside system  200  is shown as a central plant having a plurality of subplants  202 - 212 . Subplants  202 - 212  are shown to include a heater subplant  202 , a heat recovery chiller subplant  204 , a chiller subplant  206 , a cooling tower subplant  208 , a hot thermal energy storage (TES) subplant  210 , and a cold thermal energy storage (TES) subplant  212 . Subplants  202 - 212  consume resources (e.g., water, natural gas, electricity, etc.) from utilities to serve the thermal energy loads (e.g., hot water, cold water, heating, cooling, etc.) of a building or campus. For example, heater subplant  202  can be configured to heat water in a hot water loop  214  that circulates the hot water between heater subplant  202  and building  10 . Chiller subplant  206  can be configured to chill water in a cold water loop  216  that circulates the cold water between chiller subplant  206  building  10 . Heat recovery chiller subplant  204  can be configured to transfer heat from cold water loop  216  to hot water loop  214  to provide additional heating for the hot water and additional cooling for the cold water. Condenser water loop  218  can absorb heat from the cold water in chiller subplant  206  and reject the absorbed heat in cooling tower subplant  208  or transfer the absorbed heat to hot water loop  214 . Hot TES subplant  210  and cold TES subplant  212  can store hot and cold thermal energy, respectively, for subsequent use. 
     Hot water loop  214  and cold water loop  216  can deliver the heated and/or chilled water to air handlers located on the rooftop of building  10  (e.g., AHU  106 ) or to individual floors or zones of building  10  (e.g., VAV units  116 ). The air handlers push air past heat exchangers (e.g., heating coils or cooling coils) through which the water flows to provide heating or cooling for the air. The heated or cooled air can be delivered to individual zones of building  10  to serve the thermal energy loads of building  10 . The water then returns to subplants  202 - 212  to receive further heating or cooling. 
     Although subplants  202 - 212  are shown and described as heating and cooling water for circulation to a building, it is understood that any other type of working fluid (e.g., glycol, CO2, etc.) can be used in place of or in addition to water to serve the thermal energy loads. In other embodiments, subplants  202 - 212  can provide heating and/or cooling directly to the building or campus without requiring an intermediate heat transfer fluid. These and other variations to waterside system  200  are within the teachings of the present invention. 
     Each of subplants  202 - 212  can include a variety of equipment configured to facilitate the functions of the subplant. For example, heater subplant  202  is shown to include a plurality of heating elements  220  (e.g., boilers, electric heaters, etc.) configured to add heat to the hot water in hot water loop  214 . Heater subplant  202  is also shown to include several pumps  222  and  224  configured to circulate the hot water in hot water loop  214  and to control the flow rate of the hot water through individual heating elements  220 . Chiller subplant  206  is shown to include a plurality of chillers  232  configured to remove heat from the cold water in cold water loop  216 . Chiller subplant  206  is also shown to include several pumps  234  and  236  configured to circulate the cold water in cold water loop  216  and to control the flow rate of the cold water through individual chillers  232 . 
     Heat recovery chiller subplant  204  is shown to include a plurality of heat recovery heat exchangers  226  (e.g., refrigeration circuits) configured to transfer heat from cold water loop  216  to hot water loop  214 . Heat recovery chiller subplant  204  is also shown to include several pumps  228  and  230  configured to circulate the hot water and/or cold water through heat recovery heat exchangers  226  and to control the flow rate of the water through individual heat recovery heat exchangers  226 . Cooling tower subplant  208  is shown to include a plurality of cooling towers  238  configured to remove heat from the condenser water in condenser water loop  218 . Cooling tower subplant  208  is also shown to include several pumps  240  configured to circulate the condenser water in condenser water loop  218  and to control the flow rate of the condenser water through individual cooling towers  238 . 
     Hot TES subplant  210  is shown to include a hot TES tank  242  configured to store the hot water for later use. Hot TES subplant  210  can also include one or more pumps or valves configured to control the flow rate of the hot water into or out of hot TES tank  242 . Cold TES subplant  212  is shown to include cold TES tanks  244  configured to store the cold water for later use. Cold TES subplant  212  can also include one or more pumps or valves configured to control the flow rate of the cold water into or out of cold TES tanks  244 . 
     In some embodiments, one or more of the pumps in waterside system  200  (e.g., pumps  222 ,  224 ,  228 ,  230 ,  234 ,  236 , and/or  240 ) or pipelines in waterside system  200  include an isolation valve associated therewith. Isolation valves can be integrated with the pumps or positioned upstream or downstream of the pumps to control the fluid flows in waterside system  200 . In various embodiments, waterside system  200  can include more, fewer, or different types of devices and/or subplants based on the particular configuration of waterside system  200  and the types of loads served by waterside system  200 . 
     Referring now to  FIG. 3 , a block diagram of an airside system  300  is shown, according to an exemplary embodiment. In various embodiments, airside system  300  can supplement or replace airside system  130  in HVAC system  100  or can be implemented separate from HVAC system  100 . When implemented in HVAC system  100 , airside system  300  can include a subset of the HVAC devices in HVAC system  100  (e.g., AHU  106 , VAV units  116 , ducts  112 - 114 , fans, dampers, etc.) and can be located in or around building  10 . Airside system  300  can operate to heat or cool an airflow provided to building  10  using a heated or chilled fluid provided by waterside system  200 . 
     In  FIG. 3 , airside system  300  is shown to include an economizer-type air handling unit (AHU)  302 . Economizer-type AHUs vary the amount of outside air and return air used by the air handling unit for heating or cooling. For example, AHU  302  can receive return air  304  from building zone  306  via return air duct  308  and can deliver supply air  310  to building zone  306  via supply air duct  312 . In some embodiments, AHU  302  is a rooftop unit located on the roof of building  10  (e.g., AHU  106  as shown in  FIG. 1 ) or otherwise positioned to receive both return air  304  and outside air  314 . AHU  302  can be configured to operate exhaust air damper  316 , mixing damper  318 , and outside air damper  320  to control an amount of outside air  314  and return air  304  that combine to form supply air  310 . Any return air  304  that does not pass through mixing damper  318  can be exhausted from AHU  302  through exhaust damper  316  as exhaust air  322 . 
     Each of dampers  316 - 320  can be operated by an actuator. For example, exhaust air damper  316  can be operated by actuator  324 , mixing damper  318  can be operated by actuator  326 , and outside air damper  320  can be operated by actuator  328 . Actuators  324 - 328  can communicate with an AHU controller  330  via a communications link  332 . Actuators  324 - 328  can receive control signals from AHU controller  330  and can provide feedback signals to AHU controller  330 . Feedback signals can include, for example, an indication of a current actuator or damper position, an amount of torque or force exerted by the actuator, diagnostic information (e.g., results of diagnostic tests performed by actuators  324 - 328 ), status information, commissioning information, configuration settings, calibration data, and/or other types of information or data that can be collected, stored, or used by actuators  324 - 328 . AHU controller  330  can be an economizer controller configured to use one or more control algorithms (e.g., state-based algorithms, extremum seeking control (ESC) algorithms, proportional-integral (PI) control algorithms, proportional-integral-derivative (PID) control algorithms, model predictive control (MPC) algorithms, feedback control algorithms, etc.) to control actuators  324 - 328 . 
     Still referring to  FIG. 3 , AHU  302  is shown to include a cooling coil  334 , a heating coil  336 , and a fan  338  positioned within supply air duct  312 . Fan  338  can be configured to force supply air  310  through cooling coil  334  and/or heating coil  336  and provide supply air  310  to building zone  306 . AHU controller  330  can communicate with fan  338  via communications link  340  to control a flow rate of supply air  310 . In some embodiments, AHU controller  330  controls an amount of heating or cooling applied to supply air  310  by modulating a speed of fan  338 . 
     Cooling coil  334  can receive a chilled fluid from waterside system  200  (e.g., from cold water loop  216 ) via piping  342  and can return the chilled fluid to waterside system  200  via piping  344 . Valve  346  can be positioned along piping  342  or piping  344  to control a flow rate of the chilled fluid through cooling coil  334 . In some embodiments, cooling coil  334  includes multiple stages of cooling coils that can be independently activated and deactivated (e.g., by AHU controller  330 , by BMS controller  366 , etc.) to modulate an amount of cooling applied to supply air  310 . 
     Heating coil  336  can receive a heated fluid from waterside system  200  (e.g., from hot water loop  214 ) via piping  348  and can return the heated fluid to waterside system  200  via piping  350 . Valve  352  can be positioned along piping  348  or piping  350  to control a flow rate of the heated fluid through heating coil  336 . In some embodiments, heating coil  336  includes multiple stages of heating coils that can be independently activated and deactivated (e.g., by AHU controller  330 , by BMS controller  366 , etc.) to modulate an amount of heating applied to supply air  310 . 
     Each of valves  346  and  352  can be controlled by an actuator. For example, valve  346  can be controlled by actuator  354  and valve  352  can be controlled by actuator  356 . Actuators  354 - 356  can communicate with AHU controller  330  via communications links  358 - 360 . Actuators  354 - 356  can receive control signals from AHU controller  330  and can provide feedback signals to controller  330 . In some embodiments, AHU controller  330  receives a measurement of the supply air temperature from a temperature sensor  362  positioned in supply air duct  312  (e.g., downstream of cooling coil  334  and/or heating coil  336 ). AHU controller  330  can also receive a measurement of the temperature of building zone  306  from a temperature sensor  364  located in building zone  306 . 
     In some embodiments, AHU controller  330  operates valves  346  and  352  via actuators  354 - 356  to modulate an amount of heating or cooling provided to supply air  310  (e.g., to achieve a setpoint temperature for supply air  310  or to maintain the temperature of supply air  310  within a setpoint temperature range). The positions of valves  346  and  352  affect the amount of heating or cooling provided to supply air  310  by cooling coil  334  or heating coil  336  and may correlate with the amount of energy consumed to achieve a desired supply air temperature. AHU controller  330  can control the temperature of supply air  310  and/or building zone  306  by activating or deactivating coils  334 - 336 , adjusting a speed of fan  338 , or a combination of both. 
     Still referring to  FIG. 3 , airside system  300  is shown to include a building management system (BMS) controller  366  and a client device  368 . BMS controller  366  can include one or more computer systems (e.g., servers, supervisory controllers, subsystem controllers, etc.) that serve as system level controllers, application or data servers, head nodes, or master controllers for airside system  300 , waterside system  200 , HVAC system  100 , and/or other controllable systems that serve building  10 . BMS controller  366  can communicate with multiple downstream building systems or subsystems (e.g., HVAC system  100 , a security system, a lighting system, waterside system  200 , etc.) via a communications link  370  according to like or disparate protocols (e.g., LON, BACnet, etc.). In various embodiments, AHU controller  330  and BMS controller  366  can be separate (as shown in  FIG. 3 ) or integrated. In an integrated implementation, AHU controller  330  can be a software module configured for execution by a processor of BMS controller  366 . 
     In some embodiments, AHU controller  330  receives information from BMS controller  366  (e.g., commands, setpoints, operating boundaries, etc.) and provides information to BMS controller  366  (e.g., temperature measurements, valve or actuator positions, operating statuses, diagnostics, etc.). For example, AHU controller  330  can provide BMS controller  366  with temperature measurements from temperature sensors  362 - 364 , equipment on/off states, equipment operating capacities, and/or any other information that can be used by BMS controller  366  to monitor or control a variable state or condition within building zone  306 . 
     Client device  368  can include one or more human-machine interfaces or client interfaces (e.g., graphical user interfaces, reporting interfaces, text-based computer interfaces, client-facing web services, web servers that provide pages to web clients, etc.) for controlling, viewing, or otherwise interacting with HVAC system  100 , its subsystems, and/or devices. Client device  368  can be a computer workstation, a client terminal, a remote or local interface, or any other type of user interface device. Client device  368  can be a stationary terminal or a mobile device. For example, client device  368  can be a desktop computer, a computer server with a user interface, a laptop computer, a tablet, a smartphone, a PDA, or any other type of mobile or non-mobile device. Client device  368  can communicate with BMS controller  366  and/or AHU controller  330  via communications link  372 . 
     Referring now to  FIG. 4 , a block diagram of a building management system (BMS)  400  is shown, according to an exemplary embodiment. BMS  400  can be implemented in building  10  to automatically monitor and control various building functions. BMS  400  is shown to include BMS controller  366  and a plurality of building subsystems  428 . Building subsystems  428  are shown to include a building electrical subsystem  434 , an information communication technology (ICT) subsystem  436 , a security subsystem  438 , a HVAC subsystem  440 , a lighting subsystem  442 , a lift/escalators subsystem  432 , and a fire safety subsystem  430 . In various embodiments, building subsystems  428  can include fewer, additional, or alternative subsystems. For example, building subsystems  428  can also or alternatively include a refrigeration subsystem, an advertising or signage subsystem, a cooking subsystem, a vending subsystem, a printer or copy service subsystem, or any other type of building subsystem that uses controllable equipment and/or sensors to monitor or control building  10 . In some embodiments, building subsystems  428  include waterside system  200  and/or airside system  300 , as described with reference to  FIGS. 2-3 . 
     Each of building subsystems  428  can include any number of devices, controllers, and connections for completing its individual functions and control activities. HVAC subsystem  440  can include many of the same components as HVAC system  100 , as described with reference to  FIGS. 1-3 . For example, HVAC subsystem  440  can include a chiller, a boiler, any number of air handling units, economizers, field controllers, supervisory controllers, actuators, temperature sensors, and other devices for controlling the temperature, humidity, airflow, or other variable conditions within building  10 . Lighting subsystem  442  can include any number of light fixtures, ballasts, lighting sensors, dimmers, or other devices configured to controllably adjust the amount of light provided to a building space. Security subsystem  438  can include occupancy sensors, video surveillance cameras, digital video recorders, video processing servers, intrusion detection devices, access control devices and servers, or other security-related devices. 
     Still referring to  FIG. 4 , BMS controller  366  is shown to include a communications interface  407  and a BMS interface  409 . Interface  407  can facilitate communications between BMS controller  366  and external applications (e.g., monitoring and reporting applications  422 , enterprise control applications  426 , remote systems and applications  444 , applications residing on client devices  448 , etc.) for allowing user control, monitoring, and adjustment to BMS controller  366  and/or subsystems  428 . Interface  407  can also facilitate communications between BMS controller  366  and client devices  448 . BMS interface  409  can facilitate communications between BMS controller  366  and building subsystems  428  (e.g., HVAC, lighting security, lifts, power distribution, business, etc.). 
     Interfaces  407 ,  409  can be or include wired or wireless communications interfaces (e.g., jacks, antennas, transmitters, receivers, transceivers, wire terminals, etc.) for conducting data communications with building subsystems  428  or other external systems or devices. In various embodiments, communications via interfaces  407 ,  409  can be direct (e.g., local wired or wireless communications) or via a communications network  446  (e.g., a WAN, the Internet, a cellular network, etc.). For example, interfaces  407 ,  409  can include an Ethernet card and port for sending and receiving data via an Ethernet-based communications link or network. In another example, interfaces  407 ,  409  can include a WiFi transceiver for communicating via a wireless communications network. In another example, one or both of interfaces  407 ,  409  can include cellular or mobile phone communications transceivers. In one embodiment, communications interface  407  is a power line communications interface and BMS interface  409  is an Ethernet interface. In other embodiments, both communications interface  407  and BMS interface  409  are Ethernet interfaces or are the same Ethernet interface. 
     Still referring to  FIG. 4 , BMS controller  366  is shown to include a processing circuit  404  including a processor  406  and memory  408 . Processing circuit  404  can be communicably connected to BMS interface  409  and/or communications interface  407  such that processing circuit  404  and the various components thereof can send and receive data via interfaces  407 ,  409 . Processor  406  can be implemented as a general purpose processor, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable electronic processing components. 
     Memory  408  (e.g., memory, memory unit, storage device, etc.) can include one or more devices (e.g., RAM, ROM, Flash memory, hard disk storage, etc.) for storing data and/or computer code for completing or facilitating the various processes, layers and modules described in the present application. Memory  408  can be or include volatile memory or non-volatile memory. Memory  408  can include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in the present application. According to an exemplary embodiment, memory  408  is communicably connected to processor  406  via processing circuit  404  and includes computer code for executing (e.g., by processing circuit  404  and/or processor  406 ) one or more processes described herein. 
     In some embodiments, BMS controller  366  is implemented within a single computer (e.g., one server, one housing, etc.). In various other embodiments BMS controller  366  can be distributed across multiple servers or computers (e.g., that can exist in distributed locations). Further, while  FIG. 4  shows applications  422  and  426  as existing outside of BMS controller  366 , in some embodiments, applications  422  and  426  can be hosted within BMS controller  366  (e.g., within memory  408 ). 
     Still referring to  FIG. 4 , memory  408  is shown to include an enterprise integration layer  410 , an automated measurement and validation (AM&amp;V) layer  412 , a demand response (DR) layer  414 , a fault detection and diagnostics (FDD) layer  416 , an integrated control layer  418 , and a building subsystem integration later  420 . Layers  410 - 420  can be configured to receive inputs from building subsystems  428  and other data sources, determine optimal control actions for building subsystems  428  based on the inputs, generate control signals based on the optimal control actions, and provide the generated control signals to building subsystems  428 . The following paragraphs describe some of the general functions performed by each of layers  410 - 420  in BMS  400 . 
     Enterprise integration layer  410  can be configured to serve clients or local applications with information and services to support a variety of enterprise-level applications. For example, enterprise control applications  426  can be configured to provide subsystem-spanning control to a graphical user interface (GUI) or to any number of enterprise-level business applications (e.g., accounting systems, user identification systems, etc.). Enterprise control applications  426  can also or alternatively be configured to provide configuration GUIs for configuring BMS controller  366 . In yet other embodiments, enterprise control applications  426  can work with layers  410 - 420  to optimize building performance (e.g., efficiency, energy use, comfort, or safety) based on inputs received at interface  407  and/or BMS interface  409 . 
     Building subsystem integration layer  420  can be configured to manage communications between BMS controller  366  and building subsystems  428 . For example, building subsystem integration layer  420  can receive sensor data and input signals from building subsystems  428  and provide output data and control signals to building subsystems  428 . Building subsystem integration layer  420  can also be configured to manage communications between building subsystems  428 . Building subsystem integration layer  420  translate communications (e.g., sensor data, input signals, output signals, etc.) across a plurality of multi-vendor/multi-protocol systems. 
     Demand response layer  414  can be configured to optimize resource usage (e.g., electricity use, natural gas use, water use, etc.) and/or the monetary cost of such resource usage in response to satisfy the demand of building  10 . The optimization can be based on time-of-use prices, curtailment signals, energy availability, or other data received from utility providers, distributed energy generation systems  424 , from energy storage  427  (e.g., hot TES  242 , cold TES  244 , etc.), or from other sources. Demand response layer  414  can receive inputs from other layers of BMS controller  366  (e.g., building subsystem integration layer  420 , integrated control layer  418 , etc.). The inputs received from other layers can include environmental or sensor inputs such as temperature, carbon dioxide levels, relative humidity levels, air quality sensor outputs, occupancy sensor outputs, room schedules, and the like. The inputs can also include inputs such as electrical use (e.g., expressed in kWh), thermal load measurements, pricing information, projected pricing, smoothed pricing, curtailment signals from utilities, and the like. 
     According to an exemplary embodiment, demand response layer  414  includes control logic for responding to the data and signals it receives. These responses can include communicating with the control algorithms in integrated control layer  418 , changing control strategies, changing setpoints, or activating/deactivating building equipment or subsystems in a controlled manner. Demand response layer  414  can also include control logic configured to determine when to utilize stored energy. For example, demand response layer  414  can determine to begin using energy from energy storage  427  just prior to the beginning of a peak use hour. 
     In some embodiments, demand response layer  414  includes a control module configured to actively initiate control actions (e.g., automatically changing setpoints) which minimize energy costs based on one or more inputs representative of or based on demand (e.g., price, a curtailment signal, a demand level, etc.). In some embodiments, demand response layer  414  uses equipment models to determine an optimal set of control actions. The equipment models can include, for example, thermodynamic models describing the inputs, outputs, and/or functions performed by various sets of building equipment. Equipment models can represent collections of building equipment (e.g., subplants, chiller arrays, etc.) or individual devices (e.g., individual chillers, heaters, pumps, etc.). 
     Demand response layer  414  can further include or draw upon one or more demand response policy definitions (e.g., databases, XML files, etc.). The policy definitions can be edited or adjusted by a user (e.g., via a graphical user interface) so that the control actions initiated in response to demand inputs can be tailored for the user&#39;s application, desired comfort level, particular building equipment, or based on other concerns. For example, the demand response policy definitions can specify which equipment can be turned on or off in response to particular demand inputs, how long a system or piece of equipment should be turned off, what setpoints can be changed, what the allowable set point adjustment range is, how long to hold a high demand setpoint before returning to a normally scheduled setpoint, how close to approach capacity limits, which equipment modes to utilize, the energy transfer rates (e.g., the maximum rate, an alarm rate, other rate boundary information, etc.) into and out of energy storage devices (e.g., thermal storage tanks, battery banks, etc.), and when to dispatch on-site generation of energy (e.g., via fuel cells, a motor generator set, etc.). 
     Integrated control layer  418  can be configured to use the data input or output of building subsystem integration layer  420  and/or demand response later  414  to make control decisions. Due to the subsystem integration provided by building subsystem integration layer  420 , integrated control layer  418  can integrate control activities of the subsystems  428  such that the subsystems  428  behave as a single integrated supersystem. In an exemplary embodiment, integrated control layer  418  includes control logic that uses inputs and outputs from a plurality of building subsystems to provide greater comfort and energy savings relative to the comfort and energy savings that separate subsystems could provide alone. For example, integrated control layer  418  can be configured to use an input from a first subsystem to make an energy-saving control decision for a second subsystem. Results of these decisions can be communicated back to building subsystem integration layer  420 . 
     Integrated control layer  418  is shown to be logically below demand response layer  414 . Integrated control layer  418  can be configured to enhance the effectiveness of demand response layer  414  by enabling building subsystems  428  and their respective control loops to be controlled in coordination with demand response layer  414 . This configuration may advantageously reduce disruptive demand response behavior relative to conventional systems. For example, integrated control layer  418  can be configured to assure that a demand response-driven upward adjustment to the setpoint for chilled water temperature (or another component that directly or indirectly affects temperature) does not result in an increase in fan energy (or other energy used to cool a space) that would result in greater total building energy use than was saved at the chiller. 
     Integrated control layer  418  can be configured to provide feedback to demand response layer  414  so that demand response layer  414  checks that constraints (e.g., temperature, lighting levels, etc.) are properly maintained even while demanded load shedding is in progress. The constraints can also include setpoint or sensed boundaries relating to safety, equipment operating limits and performance, comfort, fire codes, electrical codes, energy codes, and the like. Integrated control layer  418  is also logically below fault detection and diagnostics layer  416  and automated measurement and validation layer  412 . Integrated control layer  418  can be configured to provide calculated inputs (e.g., aggregations) to these higher levels based on outputs from more than one building subsystem. 
     Automated measurement and validation (AM&amp;V) layer  412  can be configured to verify that control strategies commanded by integrated control layer  418  or demand response layer  414  are working properly (e.g., using data aggregated by AM&amp;V layer  412 , integrated control layer  418 , building subsystem integration layer  420 , FDD layer  416 , or otherwise). The calculations made by AM&amp;V layer  412  can be based on building system energy models and/or equipment models for individual BMS devices or subsystems. For example, AM&amp;V layer  412  can compare a model-predicted output with an actual output from building subsystems  428  to determine an accuracy of the model. 
     Fault detection and diagnostics (FDD) layer  416  can be configured to provide on-going fault detection for building subsystems  428 , building subsystem devices (i.e., building equipment), and control algorithms used by demand response layer  414  and integrated control layer  418 . FDD layer  416  can receive data inputs from integrated control layer  418 , directly from one or more building subsystems or devices, or from another data source. FDD layer  416  can automatically diagnose and respond to detected faults. The responses to detected or diagnosed faults can include providing an alert message to a user, a maintenance scheduling system, or a control algorithm configured to attempt to repair the fault or to work-around the fault. 
     FDD layer  416  can be configured to output a specific identification of the faulty component or cause of the fault (e.g., loose damper linkage) using detailed subsystem inputs available at building subsystem integration layer  420 . In other exemplary embodiments, FDD layer  416  is configured to provide “fault” events to integrated control layer  418  which executes control strategies and policies in response to the received fault events. According to an exemplary embodiment, FDD layer  416  (or a policy executed by an integrated control engine or business rules engine) can shut-down systems or direct control activities around faulty devices or systems to reduce energy waste, extend equipment life, or assure proper control response. 
     FDD layer  416  can be configured to store or access a variety of different system data stores (or data points for live data). FDD layer  416  can use some content of the data stores to identify faults at the equipment level (e.g., specific chiller, specific AHU, specific terminal unit, etc.) and other content to identify faults at component or subsystem levels. For example, building subsystems  428  can generate temporal (i.e., time-series) data indicating the performance of BMS  400  and the various components thereof. The data generated by building subsystems  428  can include measured or calculated values that exhibit statistical characteristics and provide information about how the corresponding system or process (e.g., a temperature control process, a flow control process, etc.) is performing in terms of error from its setpoint. These processes can be examined by FDD layer  416  to expose when the system begins to degrade in performance and alert a user to repair the fault before it becomes more severe. 
     Building Management System with Data Platform Services 
     Referring now to  FIG. 5 , a block diagram of another building management system (BMS)  500  is shown, according to some embodiments. BMS  500  can be configured to collect data samples from building subsystems  428  and generate raw timeseries data from the data samples. BMS  500  can process and transform the raw timeseries data using data platform services  520  to generate derived timeseries data. Throughout this disclosure, the term “derived timeseries data” is used to describe the result or output of a transformation or other timeseries processing operation performed by data platform services  520  (e.g., data aggregation, data cleansing, virtual point calculation, etc.). The derived timeseries data can be provided to various applications  530  and/or stored in local storage  514  or hosted storage  516  (e.g., as materialized views of the raw timeseries data). In some embodiments, BMS  500  separates data collection; data storage, retrieval, and analysis; and data visualization into three different layers. This allows BMS  500  to support a variety of applications  530  that use the derived timeseries data and allows new applications  530  to reuse the existing infrastructure provided by data platform services  520 . 
     Before discussing BMS  500  in greater detail, it should be noted that the components of BMS  500  can be integrated within a single device (e.g., a supervisory controller, a BMS controller, etc.) or distributed across multiple separate systems or devices. For example, the components of BMS  500  can be implemented as part of a METASYS® brand building automation system, as sold by Johnson Controls Inc. In other embodiments, some or all of the components of BMS  500  can be implemented as part of a cloud-based computing system configured to receive and process data from one or more building management systems. In other embodiments, some or all of the components of BMS  500  can be components of a subsystem level controller (e.g., a HVAC controller), a subplant controller, a device controller (e.g., AHU controller  330 , a chiller controller, etc.), a field controller, a computer workstation, a client device, or any other system or device that receives and processes data from building equipment. 
     BMS  500  can include many of the same components as BMS  400 , as described with reference to  FIG. 4 . For example, BMS  500  is shown to include a BMS interface  502  and a communications interface  504 . Interfaces  502 - 504  can include wired or wireless communications interfaces (e.g., jacks, antennas, transmitters, receivers, transceivers, wire terminals, etc.) for conducting data communications with building subsystems  428  or other external systems or devices. Communications conducted via interfaces  502 - 504  can be direct (e.g., local wired or wireless communications) or via a communications network  446  (e.g., a WAN, the Internet, a cellular network, etc.). 
     Communications interface  504  can facilitate communications between BMS  500  and external applications (e.g., remote systems and applications  444 ) for allowing user control, monitoring, and adjustment to BMS  500 . Communications interface  504  can also facilitate communications between BMS  500  and client devices  448 . BMS interface  502  can facilitate communications between BMS  500  and building subsystems  428 . BMS  500  can be configured to communicate with building subsystems  428  using any of a variety of building automation systems protocols (e.g., BACnet, Modbus, ADX, etc.). In some embodiments, BMS  500  receives data samples from building subsystems  428  and provides control signals to building subsystems  428  via BMS interface  502 . 
     Building subsystems  428  can include building electrical subsystem  434 , information communication technology (ICT) subsystem  436 , security subsystem  438 , HVAC subsystem  440 , lighting subsystem  442 , lift/escalators subsystem  432 , and/or fire safety subsystem  430 , as described with reference to  FIG. 4 . In various embodiments, building subsystems  428  can include fewer, additional, or alternative subsystems. For example, building subsystems  428  can also or alternatively include a refrigeration subsystem, an advertising or signage subsystem, a cooking subsystem, a vending subsystem, a printer or copy service subsystem, or any other type of building subsystem that uses controllable equipment and/or sensors to monitor or control building  10 . In some embodiments, building subsystems  428  include waterside system  200  and/or airside system  300 , as described with reference to  FIGS. 2-3 . Each of building subsystems  428  can include any number of devices, controllers, and connections for completing its individual functions and control activities. Building subsystems  428  can include building equipment (e.g., sensors, air handling units, chillers, pumps, valves, etc.) configured to monitor and control a building condition such as temperature, humidity, airflow, etc. 
     Still referring to  FIG. 5 , BMS  500  is shown to include a processing circuit  506  including a processor  508  and memory  510 . Processor  508  can be a general purpose or specific purpose processor, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable processing components. Processor  508  is configured to execute computer code or instructions stored in memory  510  or received from other computer readable media (e.g., CDROM, network storage, a remote server, etc.). 
     Memory  510  can include one or more devices (e.g., memory units, memory devices, storage devices, etc.) for storing data and/or computer code for completing and/or facilitating the various processes described in the present disclosure. Memory  510  can include random access memory (RAM), read-only memory (ROM), hard drive storage, temporary storage, non-volatile memory, flash memory, optical memory, or any other suitable memory for storing software objects and/or computer instructions. Memory  510  can include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in the present disclosure. Memory  510  can be communicably connected to processor  508  via processing circuit  506  and can include computer code for executing (e.g., by processor  508 ) one or more processes described herein. When processor  508  executes instructions stored in memory  510 , processor  508  generally configures processing circuit  506  to complete such activities. 
     Still referring to  FIG. 5 , BMS  500  is shown to include a data collector  512 . Data collector  512  is shown receiving data samples from building subsystems  428  via BMS interface  502 . In some embodiments, the data samples include data values for various data points. The data values can be measured or calculated values, depending on the type of data point. For example, a data point received from a temperature sensor can include a measured data value indicating a temperature measured by the temperature sensor. A data point received from a chiller controller can include a calculated data value indicating a calculated efficiency of the chiller. Data collector  512  can receive data samples from multiple different devices within building subsystems  428 . 
     The data samples can include one or more attributes that describe or characterize the corresponding data points. For example, the data samples can include a name attribute defining a point name or ID (e.g., “B1F4R2.T-Z”), a device attribute indicating a type of device from which the data samples is received (e.g., temperature sensor, humidity sensor, chiller, etc.), a unit attribute defining a unit of measure associated with the data value (e.g., ° F., ° C., kPA, etc.), and/or any other attribute that describes the corresponding data point or provides contextual information regarding the data point. The types of attributes included in each data point can depend on the communications protocol used to send the data samples to BMS  500 . For example, data samples received via the ADX protocol or BACnet protocol can include a variety of descriptive attributes along with the data value, whereas data samples received via the Modbus protocol may include a lesser number of attributes (e.g., only the data value without any corresponding attributes). 
     In some embodiments, each data sample is received with a timestamp indicating a time at which the corresponding data value was measured or calculated. In other embodiments, data collector  512  adds timestamps to the data samples based on the times at which the data samples are received. Data collector  512  can generate raw timeseries data for each of the data points for which data samples are received. Each timeseries can include a series of data values for the same data point and a timestamp for each of the data values. For example, a timeseries for a data point provided by a temperature sensor can include a series of temperature values measured by the temperature sensor and the corresponding times at which the temperature values were measured. An example of a timeseries which can be generated by data collector  512  is as follows: 
       [&lt;key,timestamp 1 ,value 1 &gt;,&lt;key,timestamp 2 ,value 2 &gt;,&lt;key,timestamp 3 ,value 3 &gt;] 
     where key is an identifier of the source of the raw data samples (e.g., timeseries ID, sensor ID, etc.), timestamp identifies the time at which the ith sample was collected, and value r  indicates the value of the ith sample. 
     Data collector  512  can add timestamps to the data samples or modify existing timestamps such that each data sample includes a local timestamp. Each local timestamp indicates the local time at which the corresponding data sample was measured or collected and can include an offset relative to universal time. The local timestamp indicates the local time at the location the data point was measured at the time of measurement. The offset indicates the difference between the local time and a universal time (e.g., the time at the international date line). For example, a data sample collected in a time zone that is six hours behind universal time can include a local timestamp (e.g., Timestamp=2016-03-18T14:10:02) and an offset indicating that the local timestamp is six hours behind universal time (e.g., Offset=−6:00). The offset can be adjusted (e.g., +1:00 or −1:00) depending on whether the time zone is in daylight savings time when the data sample is measured or collected. 
     The combination of the local timestamp and the offset provides a unique timestamp across daylight saving time boundaries. This allows an application using the timeseries data to display the timeseries data in local time without first converting from universal time. The combination of the local timestamp and the offset also provides enough information to convert the local timestamp to universal time without needing to look up a schedule of when daylight savings time occurs. For example, the offset can be subtracted from the local timestamp to generate a universal time value that corresponds to the local timestamp without referencing an external database and without requiring any other information. 
     In some embodiments, data collector  512  organizes the raw timeseries data. Data collector  512  can identify a system or device associated with each of the data points. For example, data collector  512  can associate a data point with a temperature sensor, an air handler, a chiller, or any other type of system or device. In various embodiments, data collector uses the name of the data point, a range of values of the data point, statistical characteristics of the data point, or other attributes of the data point to identify a particular system or device associated with the data point. Data collector  512  can then determine how that system or device relates to the other systems or devices in the building site. For example, data collector  512  can determine that the identified system or device is part of a larger system (e.g., a HVAC system) or serves a particular space (e.g., a particular building, a room or zone of the building, etc.). In some embodiments, data collector  512  uses or creates an entity graph when organizing the timeseries data. An example of such an entity graph is described in greater detail with reference to  FIG. 10A . 
     Data collector  512  can provide the raw timeseries data to data platform services  520  and/or store the raw timeseries data in local storage  514  or hosted storage  516 . As shown in  FIG. 5 , local storage  514  can be data storage internal to BMS  500  (e.g., within memory  510 ) or other on-site data storage local to the building site at which the data samples are collected. Hosted storage  516  can include a remote database, cloud-based data hosting, or other remote data storage. For example, hosted storage  516  can include remote data storage located off-site relative to the building site at which the data samples are collected. Local storage  514  and hosted storage  516  can be configured to store the raw timeseries data obtained by data collector  512 , the derived timeseries data generated by data platform services  520 , and/or directed acyclic graphs (DAGs) used by data platform services  520  to process the timeseries data. 
     Still referring to  FIG. 5 , BMS  500  is shown to include data platform services  520 . Data platform services  520  can receive the raw timeseries data from data collector  512  and/or retrieve the raw timeseries data from local storage  514  or hosted storage  516 . Data platform services  520  can include a variety of services configured to analyze, process, and transform the raw timeseries data. For example, data platform services  520  are shown to include a security service  522 , an analytics service  524 , an entity service  526 , and a timeseries service  528 . Security service  522  can assign security attributes to the raw timeseries data to ensure that the timeseries data are only accessible to authorized individuals, systems, or applications. Entity service  524  can assign entity information to the timeseries data to associate data points with a particular system, device, or space. Timeseries service  528  and analytics service  524  can apply various transformations, operations, or other functions to the raw timeseries data to generate derived timeseries data. 
     In some embodiments, timeseries service  528  aggregates predefined intervals of the raw timeseries data (e.g., quarter-hourly intervals, hourly intervals, daily intervals, monthly intervals, etc.) to generate new derived timeseries of the aggregated values. These derived timeseries can be referred to as “data rollups” since they are condensed versions of the raw timeseries data. The data rollups generated by timeseries service  528  provide an efficient mechanism for applications  530  to query the timeseries data. For example, applications  530  can construct visualizations of the timeseries data (e.g., charts, graphs, etc.) using the pre-aggregated data rollups instead of the raw timeseries data. This allows applications  530  to simply retrieve and present the pre-aggregated data rollups without requiring applications  530  to perform an aggregation in response to the query. Since the data rollups are pre-aggregated, applications  530  can present the data rollups quickly and efficiently without requiring additional processing at query time to generate aggregated timeseries values. 
     In some embodiments, timeseries service  528  calculates virtual points based on the raw timeseries data and/or the derived timeseries data. Virtual points can be calculated by applying any of a variety of mathematical operations (e.g., addition, subtraction, multiplication, division, etc.) or functions (e.g., average value, maximum value, minimum value, thermodynamic functions, linear functions, nonlinear functions, etc.) to the actual data points represented by the timeseries data. For example, timeseries service  528  can calculate a virtual data point (pointID 3 ) by adding two or more actual data points (pointID 1  and pointID 2 ) (e.g., pointID 3 =pointID 1 +pointID 2 ). As another example, timeseries service  528  can calculate an enthalpy data point (pointID 4 ) based on a measured temperature data point (pointID 5 ) and a measured pressure data point (pointID 6 ) (e.g., pointID 4 =enthalpy(pointID 5 , pointID 6 )). The virtual data points can be stored as derived timeseries data. 
     Applications  530  can access and use the virtual data points in the same manner as the actual data points. Applications  530  do not need to know whether a data point is an actual data point or a virtual data point since both types of data points can be stored as derived timeseries data and can be handled in the same manner by applications  530 . In some embodiments, the derived timeseries are stored with attributes designating each data point as either a virtual data point or an actual data point. Such attributes allow applications  530  to identify whether a given timeseries represents a virtual data point or an actual data point, even though both types of data points can be handled in the same manner by applications  530 . These and other features of timeseries service  528  are described in greater detail with reference to  FIG. 6 . 
     In some embodiments, analytics service  524  analyzes the raw timeseries data and/or the derived timeseries data to detect faults. Analytics service  524  can apply a set of fault detection rules to the timeseries data to determine whether a fault is detected at each interval of the timeseries. Fault detections can be stored as derived timeseries data. For example, analytics service  524  can generate a new fault detection timeseries with data values that indicate whether a fault was detected at each interval of the timeseries. An example of such a fault detection timeseries is described in greater detail with reference to  FIG. 9B . The fault detection timeseries can be stored as derived timeseries data along with the raw timeseries data in local storage  514  or hosted storage  516 . 
     Still referring to  FIG. 5 , BMS  500  is shown to include several applications  530  including an energy management application  532 , monitoring and reporting applications  534 , and enterprise control applications  536 . Although only a few applications  530  are shown, it is contemplated that applications  530  can include any of a variety of applications configured to use the derived timeseries generated by data platform services  520 . In some embodiments, applications  530  exist as a separate layer of BMS  500  (i.e., separate from data platform services  520  and data collector  512 ). This allows applications  530  to be isolated from the details of how the derived timeseries are generated. In other embodiments, applications  530  can exist as remote applications that run on remote systems or devices (e.g., remote systems and applications  444 , client devices  448 ). 
     Applications  530  can use the derived timeseries data to perform a variety data visualization, monitoring, and/or control activities. For example, energy management application  532  and monitoring and reporting application  534  can use the derived timeseries data to generate user interfaces (e.g., charts, graphs, etc.) that present the derived timeseries data to a user. In some embodiments, the user interfaces present the raw timeseries data and the derived data rollups in a single chart or graph. For example, a dropdown selector can be provided to allow a user to select the raw timeseries data or any of the data rollups for a given data point. Several examples of user interfaces that can be generated based on the derived timeseries data are described in U.S. patent application Ser. No. 15/182,579 filed Jun. 14, 2016, and U.S. Provisional Patent Application No. 62/446,284 filed Jan. 13, 2017. The entire disclosures of both these patent applications are incorporated by reference herein. 
     Enterprise control application  536  can use the derived timeseries data to perform various control activities. For example, enterprise control application  536  can use the derived timeseries data as input to a control algorithm (e.g., a state-based algorithm, an extremum seeking control (ESC) algorithm, a proportional-integral (PI) control algorithm, a proportional-integral-derivative (PID) control algorithm, a model predictive control (MPC) algorithm, a feedback control algorithm, etc.) to generate control signals for building subsystems  428 . In some embodiments, building subsystems  428  use the control signals to operate building equipment. Operating the building equipment can affect the measured or calculated values of the data samples provided to BMS  500 . Accordingly, enterprise control application  536  can use the derived timeseries data as feedback to control the systems and devices of building subsystems  428 . 
     Timeseries Data Platform Service 
     Referring now to  FIG. 6 , a block diagram illustrating timeseries service  528  in greater detail is shown, according to some embodiments. Timeseries service  528  is shown to include a timeseries web service  602 , an events service  603 , a timeseries processing engine  604 , and a timeseries storage interface  616 . Timeseries web service  602  can be configured to interact with web-based applications to send and/or receive timeseries data. In some embodiments, timeseries web service  602  provides timeseries data to web-based applications. For example, if one or more of applications  530  are web-based applications, timeseries web service  602  can provide derived timeseries data and raw timeseries data to the web-based applications. In some embodiments, timeseries web service  602  receives raw timeseries data from a web-based data collector. For example, if data collector  512  is a web-based application, timeseries web service  602  can receive data samples or raw timeseries data from data collector  512 . 
     Timeseries storage interface  616  can be configured to store and read samples of various timeseries (e.g., raw timeseries data and derived timeseries data) and eventseries (described in greater detail below). Timeseries storage interface  616  can interact with local storage  514  and/or hosted storage  516 . For example, timeseries storage interface  616  can retrieve timeseries data from a local timeseries database  628  within local storage  514  or from a hosted timeseries database  636  within hosted storage  516 . In some embodiments, timeseries storage interface  616  reads samples from a specified start time or start position in the timeseries to a specified stop time or a stop position in the timeseries. Similarly, timeseries storage interface  616  can retrieve eventseries data from a local eventseries database  629  within local storage  514  or from a hosted eventseries database  637  within hosted storage  516 . Timeseries storage interface  616  can also store timeseries data in local timeseries database  628  or hosted timeseries database  636  and can store eventseries data in local eventseries database  629  or hosted eventseries database  637 . Advantageously, timeseries storage interface  616  provides a consistent interface which enables logical data independence. 
     In some embodiments, timeseries storage interface  616  stores timeseries as lists of data samples, organized by time. For example, timeseries storage interface  616  can store timeseries in the following format: 
       [&lt;key,timestamp 1 ,value 1 &gt;,&lt;key,timestamp 2 ,value 2 &gt;,&lt;key,timestamp 3 ,value 3 &gt;] 
     where key is an identifier of the source of the data samples (e.g., timeseries ID, sensor ID, etc.), timestamp identifies a time associated with the ith sample, and value r  indicates the value of the ith sample. 
     In some embodiments, timeseries storage interface  616  stores eventseries as lists of events having a start time, an end time, and a state. For example, timeseries storage interface  616  can store eventseries in the following format: 
       [&lt;eventID 1 ,start_timestamp 1 ,end_timestamp 1 ,state 1 &gt;, . . . ,&lt;eventID N ,start_timestamp N ,end_timestamp N ,state N &gt;] 
     where eventID i  is an identifier of the ith event, start_timestamp i  is the time at which the ith event started, end_timestamp i  is the time at which the ith event ended, state describes a state or condition associated with the ith event (e.g., cold, hot, warm, etc.), and N is the total number of events in the eventseries. 
     In some embodiments, timeseries storage interface  616  stores timeseries and eventseries in a tabular format. Timeseries storage interface  616  can store timeseries and eventseries in various tables having a column for each attribute of the timeseries/eventseries samples (e.g., key, timestamp, value). The timeseries tables can be stored in local timeseries database  628  and/or hosted timeseries database  636 , whereas the eventseries tables can be stored in local eventseries database  629  and/or hosted eventseries database  637 . In some embodiments, timeseries storage interface  616  caches older data to local storage  514  or hosted storage  516  but stores newer data in RAM. This may improve read performance when the newer data are requested for processing. 
     In some embodiments, timeseries storage interface  616  omits one or more of the attributes when storing the timeseries samples. For example, timeseries storage interface  616  may not need to repeatedly store the key or timeseries ID for each sample in the timeseries. In some embodiments, timeseries storage interface  616  omits timestamps from one or more of the samples. If samples of a particular timeseries have timestamps at regular intervals (e.g., one sample each minute), timeseries storage interface  616  can organize the samples by timestamps and store the values of the samples in a row. The timestamp of the first sample can be stored along with the interval between the timestamps. Timeseries storage interface  616  can determine the timestamp of any sample in the row based on the timestamp of the first sample and the position of the sample in the row. 
     In some embodiments, timeseries storage interface  616  stores one or more samples with an attribute indicating a change in value relative to the previous sample value. The change in value can replace the actual value of the sample when the sample is stored in local timeseries database  628  or hosted timeseries database  636 . This allows timeseries storage interface  616  to use fewer bits when storing samples and their corresponding values. Timeseries storage interface  616  can determine the value of any sample based on the value of the first sample and the change in value of each successive sample. 
     In some embodiments, timeseries storage interface  616  creates containers or data objects in which samples of timeseries data can be stored. The containers can be JSON objects or other types of containers configured to store one or more timeseries samples and/or eventseries samples. Timeseries storage interface  616  can be configured to add samples to the containers and read samples from the containers. For example, timeseries storage interface  616  can receive a set of samples from data collector  512 , timeseries web service  602 , events service  603 , and/or timeseries processing engine  604 . Timeseries storage interface  616  can add the set of samples to a container and send the container to local storage  514  or hosted storage  516 . 
     Timeseries storage interface  616  can use containers when reading samples from local storage  514  or hosted storage  516 . For example, timeseries storage interface  616  can retrieve a set of samples from local storage  514  or hosted storage  516  and add the samples to a container. In some embodiments, the set of samples include all samples within a specified time period (e.g., samples with timestamps in the specified time period) or eventseries samples having a specified state. Timeseries storage interface  616  can provide the container of samples to timeseries web service  602 , events service  603 , timeseries processing engine  604 , applications  530 , and/or other components configured to use the timeseries/eventseries samples. 
     Still referring to  FIG. 6 , timeseries processing engine  604  is shown to include several timeseries operators  606 . Timeseries operators  606  can be configured to apply various operations, transformations, or functions to one or more input timeseries to generate output timeseries and/or eventseries. The input timeseries can include raw timeseries data and/or derived timeseries data. Timeseries operators  606  can be configured to calculate aggregate values, averages, or apply other mathematical operations to the input timeseries. In some embodiments, timeseries operators  606  generate virtual point timeseries by combining two or more input timeseries (e.g., adding the timeseries together), creating multiple output timeseries from a single input timeseries, or applying mathematical operations to the input timeseries. In some embodiments, timeseries operators  606  perform data cleansing operations or deduplication operations on an input timeseries. In some embodiments, timeseries operators  606  use the input timeseries to generate eventseries based on the values of the timeseries samples (described in greater detail below). The output timeseries can be stored as derived timeseries data in local storage  514  and/or hosted storage  516 . Similarly, the eventseries can be stored as eventseries data in local storage  514  and/or hosted storage  516 . 
     In some embodiments, timeseries operators  606  do not change or replace the raw timeseries data, but rather generate various “views” of the raw timeseries data. The views can be queried in the same manner as the raw timeseries data. For example, samples can be read from the raw timeseries data, transformed to create the view, and then provided as an output. Because the transformations used to create the views can be computationally expensive, the views can be stored as “materialized views” in local timeseries database  628  or hosted timeseries database  636 . These materialized views are referred to as derived timeseries data throughout the present disclosure. 
     Timeseries operators  606  can be configured to run at query time (e.g., when a request for derived timeseries data is received) or prior to query time (e.g., when new raw data samples are received, in response to a defined event or trigger, etc.). This flexibility allows timeseries operators  606  to perform some or all of their operations ahead of time and/or in response to a request for specific derived data timeseries. For example, timeseries operators  606  can be configured to pre-process one or more timeseries that are read frequently to ensure that the timeseries are updated whenever new data samples are received. However, timeseries operators  606  can be configured to wait until query time to process one or more timeseries that are read infrequently to avoid performing unnecessary processing operations. 
     In some embodiments, timeseries operators  606  are triggered in a particular sequence defined by a directed acyclic graph (DAG). The DAG may define a workflow or sequence of operations or transformations to apply to one or more input timeseries. For example, the DAG for a raw data timeseries may include a data cleansing operation, an aggregation operation, and a summation operation (e.g., adding two raw data timeseries to create a virtual point timeseries). The DAGs can be stored in a local DAG database  630  within local storage  514 , in a hosted DAG database  638  within hosted storage  516 , or internally within timeseries processing engine  604 . DAGs can be retrieved by workflow manager  622  and used to determine how and when to process incoming data samples. Exemplary systems and methods for creating and using DAGs are described in greater detail below. 
     Timeseries operators  606  can perform aggregations for dashboards, cleansing operations, logical operations for rules and fault detection, machine learning predictions or classifications, call out to external services, or any of a variety of other operations which can be applied to timeseries data. The operations performed by timeseries operators  606  are not limited to sensor data. Timeseries operators  606  can also operate on event data or function as a billing engine for a consumption or tariff-based billing system. 
     Sample Aggregation 
     Still referring to  FIG. 6 , timeseries operators  606  are shown to include a sample aggregator  608 . Sample aggregator  608  can be configured to generate derived data rollups from the raw timeseries data. For each data point, sample aggregator  608  can aggregate a set of data values having timestamps within a predetermined time interval (e.g., a quarter-hour, an hour, a day, etc.) to generate an aggregate data value for the predetermined time interval. For example, the raw timeseries data for a particular data point may have a relatively short interval (e.g., one minute) between consecutive samples of the data point. Sample aggregator  608  can generate a data rollup from the raw timeseries data by aggregating all of the samples of the data point having timestamps within a relatively longer interval (e.g., a quarter-hour) into a single aggregated value that represents the longer interval. 
     For some types of timeseries, sample aggregator  608  performs the aggregation by averaging all of the samples of the data point having timestamps within the longer interval. Aggregation by averaging can be used to calculate aggregate values for timeseries of non-cumulative variables such as measured value. For other types of timeseries, sample aggregator  608  performs the aggregation by summing all of the samples of the data point having timestamps within the longer interval. Aggregation by summation can be used to calculate aggregate values for timeseries of cumulative variables such as the number of faults detected since the previous sample. 
     Referring now to  FIGS. 7A-7B , a block diagram  700  and a data table  750  illustrating an aggregation technique which can be used by sample aggregator  608  is shown, according to some embodiments. In  FIG. 7A , a data point  702  is shown. Data point  702  is an example of a measured data point for which timeseries values can be obtained. For example, data point  702  is shown as an outdoor air temperature point and has values which can be measured by a temperature sensor. Although a specific type of data point  702  is shown in  FIG. 7A , it should be understood that data point  702  can be any type of measured or calculated data point. Timeseries values of data point  702  can be collected by data collector  512  and assembled into a raw data timeseries  704 . 
     As shown in  FIG. 7B , the raw data timeseries  704  includes a timeseries of data samples, each of which is shown as a separate row in data table  750 . Each sample of raw data timeseries  704  is shown to include a timestamp and a data value. The timestamps of raw data timeseries  704  are ten minutes and one second apart, indicating that the sampling interval of raw data timeseries  704  is ten minutes and one second. For example, the timestamp of the first data sample is shown as 2015-12-31T23:10:00 indicating that the first data sample of raw data timeseries  704  was collected at 11:10:00 PM on Dec. 31, 2015. The timestamp of the second data sample is shown as 2015-12-31T23:20:01 indicating that the second data sample of raw data timeseries  704  was collected at 11:20:01 PM on Dec. 31, 2015. In some embodiments, the timestamps of raw data timeseries  704  are stored along with an offset relative to universal time, as previously described. The values of raw data timeseries  704  start at a value of 10 and increase by 10 with each sample. For example, the value of the second sample of raw data timeseries  704  is 20, the value of the third sample of raw data timeseries  704  is 30, etc. 
     In  FIG. 7A , several data rollup timeseries  706 - 714  are shown. Data rollup timeseries  706 - 714  can be generated by sample aggregator  608  and stored as derived timeseries data. The data rollup timeseries  706 - 714  include an average quarter-hour timeseries  706 , an average hourly timeseries  708 , an average daily timeseries  710 , an average monthly timeseries  712 , and an average yearly timeseries  714 . Each of the data rollup timeseries  706 - 714  is dependent upon a parent timeseries. In some embodiments, the parent timeseries for each of the data rollup timeseries  706 - 714  is the timeseries with the next shortest duration between consecutive timeseries values. For example, the parent timeseries for average quarter-hour timeseries  706  is raw data timeseries  704 . Similarly, the parent timeseries for average hourly timeseries  708  is average quarter-hour timeseries  706 ; the parent timeseries for average daily timeseries  710  is average hourly timeseries  708 ; the parent timeseries for average monthly timeseries  712  is average daily timeseries  710 ; and the parent timeseries for average yearly timeseries  714  is average monthly timeseries  712 . 
     Sample aggregator  608  can generate each of the data rollup timeseries  706 - 714  from the timeseries values of the corresponding parent timeseries. For example, sample aggregator  608  can generate average quarter-hour timeseries  706  by aggregating all of the samples of data point  702  in raw data timeseries  704  that have timestamps within each quarter-hour. Similarly, sample aggregator  608  can generate average hourly timeseries  708  by aggregating all of the timeseries values of average quarter-hour timeseries  706  that have timestamps within each hour. Sample aggregator  608  can generate average daily timeseries  710  by aggregating all of the time series values of average hourly timeseries  708  that have timestamps within each day. Sample aggregator  608  can generate average monthly timeseries  712  by aggregating all of the time series values of average daily timeseries  710  that have timestamps within each month. Sample aggregator  608  can generate average yearly timeseries  714  by aggregating all of the time series values of average monthly timeseries  712  that have timestamps within each year. 
     In some embodiments, the timestamps for each sample in the data rollup timeseries  706 - 714  are the beginnings of the aggregation interval used to calculate the value of the sample. For example, the first data sample of average quarter-hour timeseries  706  is shown to include the timestamp 2015-12-31T23:00:00. This timestamp indicates that the first data sample of average quarter-hour timeseries  706  corresponds to an aggregation interval that begins at 11:00:00 PM on Dec. 31, 2015. Since only one data sample of raw data timeseries  704  occurs during this interval, the value of the first data sample of average quarter-hour timeseries  706  is the average of a single data value (i.e., average(10)=10). The same is true for the second data sample of average quarter-hour timeseries  706  (i.e., average(20)=20). 
     The third data sample of average quarter-hour timeseries  706  is shown to include the timestamp 2015-12-31T23:30:00. This timestamp indicates that the third data sample of average quarter-hour timeseries  706  corresponds to an aggregation interval that begins at 11:30:00 PM on Dec. 31, 2015. Since each aggregation interval of average quarter-hour timeseries  706  is a quarter-hour in duration, the end of the aggregation interval is 11:45:00 PM on Dec. 31, 2015. This aggregation interval includes two data samples of raw data timeseries  704  (i.e., the third raw data sample having a value of 30 and the fourth raw data sample having a value of 40). Sample aggregator  608  can calculate the value of the third sample of average quarter-hour timeseries  706  by averaging the values of the third raw data sample and the fourth raw data sample (i.e., average(30, 40)=35). Accordingly, the third sample of average quarter-hour timeseries  706  has a value of 35. Sample aggregator  608  can calculate the remaining values of average quarter-hour timeseries  706  in a similar manner. 
     Still referring to  FIG. 7B , the first data sample of average hourly timeseries  708  is shown to include the timestamp 2015-12-31T23:00:00. This timestamp indicates that the first data sample of average hourly timeseries  708  corresponds to an aggregation interval that begins at 11:00:00 PM on Dec. 31, 2015. Since each aggregation interval of average hourly timeseries  708  is an hour in duration, the end of the aggregation interval is 12:00:00 AM on Jan. 1, 2016. This aggregation interval includes the first four samples of average quarter-hour timeseries  706 . Sample aggregator  608  can calculate the value of the first sample of average hourly timeseries  708  by averaging the values of the first four values of average quarter-hour timeseries  706  (i.e., average(10, 20, 35, 50)=28.8). Accordingly, the first sample of average hourly timeseries  708  has a value of 28.8. Sample aggregator  608  can calculate the remaining values of average hourly timeseries  708  in a similar manner. 
     The first data sample of average daily timeseries  710  is shown to include the timestamp 2015-12-31T00:00:00. This timestamp indicates that the first data sample of average daily timeseries  710  corresponds to an aggregation interval that begins at 12:00:00 AM on Dec. 31, 2015. Since each aggregation interval of the average daily timeseries  710  is a day in duration, the end of the aggregation interval is 12:00:00 AM on Jan. 1, 2016. Only one data sample of average hourly timeseries  708  occurs during this interval. Accordingly, the value of the first data sample of average daily timeseries  710  is the average of a single data value (i.e., average(28.8)=28.8). The same is true for the second data sample of average daily timeseries  710  (i.e., average(87.5)=87.5). 
     In some embodiments, sample aggregator  608  stores each of the data rollup timeseries  706 - 714  in a single data table (e.g., data table  750 ) along with raw data timeseries  704 . This allows applications  530  to retrieve all of the timeseries  704 - 714  quickly and efficiently by accessing a single data table. In other embodiments, sample aggregator  608  can store the various timeseries  704 - 714  in separate data tables which can be stored in the same data storage device (e.g., the same database) or distributed across multiple data storage devices. In some embodiments, sample aggregator  608  stores data timeseries  704 - 714  in a format other than a data table. For example, sample aggregator  608  can store timeseries  704 - 714  as vectors, as a matrix, as a list, or using any of a variety of other data storage formats. 
     In some embodiments, sample aggregator  608  automatically updates the data rollup timeseries  706 - 714  each time a new raw data sample is received. Updating the data rollup timeseries  706 - 714  can include recalculating the aggregated values based on the value and timestamp of the new raw data sample. When a new raw data sample is received, sample aggregator  608  can determine whether the timestamp of the new raw data sample is within any of the aggregation intervals for the samples of the data rollup timeseries  706 - 714 . For example, if a new raw data sample is received with a timestamp of 2016-01-01T00:52:00, sample aggregator  608  can determine that the new raw data sample occurs within the aggregation interval beginning at timestamp 2016-01-01T00:45:00 for average quarter-hour timeseries  706 . Sample aggregator  608  can use the value of the new raw data point (e.g., value=120) to update the aggregated value of the final data sample of average quarter-hour timeseries  706  (i.e., average(110, 120)=115). 
     If the new raw data sample has a timestamp that does not occur within any of the previous aggregation intervals, sample aggregator  608  can create a new data sample in average quarter-hour timeseries  706 . The new data sample in average quarter-hour timeseries  706  can have a new data timestamp defining the beginning of an aggregation interval that includes the timestamp of the new raw data sample. For example, if the new raw data sample has a timestamp of 2016-01-01T01:00:11, sample aggregator  608  can determine that the new raw data sample does not occur within any of the aggregation intervals previously established for average quarter-hour timeseries  706 . Sample aggregator  608  can generate a new data sample in average quarter-hour timeseries  706  with the timestamp 2016-01-01T01:00:00 and can calculate the value of the new data sample in average quarter-hour timeseries  706  based on the value of the new raw data sample, as previously described. 
     Sample aggregator  608  can update the values of the remaining data rollup timeseries  708 - 714  in a similar manner. For example, sample aggregator  608  determine whether the timestamp of the updated data sample in average quarter-hour timeseries is within any of the aggregation intervals for the samples of average hourly timeseries  708 . Sample aggregator  608  can determine that the timestamp 2016-01-01T00:45:00 occurs within the aggregation interval beginning at timestamp 2016-01-01T00:00:00 for average hourly timeseries  708 . Sample aggregator  608  can use the updated value of the final data sample of average quarter-hour timeseries  706  (e.g., value=115) to update the value of the second sample of average hourly timeseries  708  (i.e., average(65, 80, 95, 115)=88.75). Sample aggregator  608  can use the updated value of the final data sample of average hourly timeseries  708  to update the final sample of average daily timeseries  710  using the same technique. 
     In some embodiments, sample aggregator  608  updates the aggregated data values of data rollup timeseries  706 - 714  each time a new raw data sample is received. Updating each time a new raw data sample is received ensures that the data rollup timeseries  706 - 714  always reflect the most recent data samples. In other embodiments, sample aggregator  608  updates the aggregated data values of data rollup timeseries  706 - 714  periodically at predetermined update intervals (e.g., hourly, daily, etc.) using a batch update technique. Updating periodically can be more efficient and require less data processing than updating each time a new data sample is received, but can result in aggregated data values that are not always updated to reflect the most recent data samples. 
     In some embodiments, sample aggregator  608  is configured to cleanse raw data timeseries  704 . Cleansing raw data timeseries  704  can include discarding exceptionally high or low data. For example, sample aggregator  608  can identify a minimum expected data value and a maximum expected data value for raw data timeseries  704 . Sample aggregator  608  can discard data values outside this range as bad data. In some embodiments, the minimum and maximum expected values are based on attributes of the data point represented by the timeseries. For example, data point  702  represents a measured outdoor air temperature and therefore has an expected value within a range of reasonable outdoor air temperature values for a given geographic location (e.g., between −20° F. and 110° F.). Sample aggregator  608  can discard a data value of 330 for data point  702  since a temperature value of 330° F. is not reasonable for a measured outdoor air temperature. 
     In some embodiments, sample aggregator  608  identifies a maximum rate at which a data point can change between consecutive data samples. The maximum rate of change can be based on physical principles (e.g., heat transfer principles), weather patterns, or other parameters that limit the maximum rate of change of a particular data point. For example, data point  702  represents a measured outdoor air temperature and therefore can be constrained to have a rate of change less than a maximum reasonable rate of change for outdoor temperature (e.g., five degrees per minute). If two consecutive data samples of the raw data timeseries  704  have values that would require the outdoor air temperature to change at a rate in excess of the maximum expected rate of change, sample aggregator  608  can discard one or both of the data samples as bad data. 
     Sample aggregator  608  can perform any of a variety of data cleansing operations to identify and discard bad data samples. Several examples of data cleansing operations which can be performed by sample aggregator  608  are described in U.S. patent application Ser. No. 13/631,301 filed Sep. 28, 2012, the entire disclosure of which is incorporated by reference herein. In some embodiments, sample aggregator  608  performs the data cleansing operations for raw data timeseries  704  before generating the data rollup timeseries  706 - 714 . This ensures that raw data timeseries  704  used to generate data rollup timeseries  706 - 714  does not include any bad data samples. Accordingly, the data rollup timeseries  706 - 714  do not need to be re-cleansed after the aggregation is performed. 
     Virtual Points 
     Referring again to  FIG. 6 , timeseries operators  606  are shown to include a virtual point calculator  610 . Virtual point calculator  610  is configured to create virtual data points and calculate timeseries values for the virtual data points. A virtual data point is a type of calculated data point derived from one or more actual data points. In some embodiments, actual data points are measured data points, whereas virtual data points are calculated data points. Virtual data points can be used as substitutes for actual sensor data when the sensor data desired for a particular application does not exist, but can be calculated from one or more actual data points. For example, a virtual data point representing the enthalpy of a refrigerant can be calculated using actual data points measuring the temperature and pressure of the refrigerant. Virtual data points can also be used to provide timeseries values for calculated quantities such as efficiency, coefficient of performance, and other variables that cannot be directly measured. 
     Virtual point calculator  610  can calculate virtual data points by applying any of a variety of mathematical operations or functions to actual data points or other virtual data points. For example, virtual point calculator  610  can calculate a virtual data point (pointID 3 ) by adding two or more actual data points (pointID 1  and pointID 2 ) (e.g., pointID 3 =pointID 1 +pointID 2 ). As another example, virtual point calculator  610  can calculate an enthalpy data point (pointID 4 ) based on a measured temperature data point (pointID 5 ) and a measured pressure data point (pointID 6 ) (e.g., pointID 4 =enthalpy(pointID 5 , pointID 6 )). In some instances, a virtual data point can be derived from a single actual data point. For example, virtual point calculator  610  can calculate a saturation temperature (pointID 7 ) of a known refrigerant based on a measured refrigerant pressure (pointID 8 ) (e.g., pointID 7 =T sat (pointID 8 )). In general, virtual point calculator  610  can calculate the timeseries values of a virtual data point using the timeseries values of one or more actual data points and/or the timeseries values of one or more other virtual data points. 
     In some embodiments, virtual point calculator  610  uses a set of virtual point rules to calculate the virtual data points. The virtual point rules can define one or more input data points (e.g., actual or virtual data points) and the mathematical operations that should be applied to the input data point(s) to calculate each virtual data point. The virtual point rules can be provided by a user, received from an external system or device, and/or stored in memory  510 . Virtual point calculator  610  can apply the set of virtual point rules to the timeseries values of the input data points to calculate timeseries values for the virtual data points. The timeseries values for the virtual data points can be stored as derived timeseries data in local timeseries database  628  and/or hosted timeseries database  636 . 
     Virtual point calculator  610  can calculate virtual data points using the values of raw data timeseries  704  and/or the aggregated values of the data rollup timeseries  706 - 714 . In some embodiments, the input data points used to calculate a virtual data point are collected at different sampling times and/or sampling rates. Accordingly, the samples of the input data points may not be synchronized with each other, which can lead to ambiguity in which samples of the input data points should be used to calculate the virtual data point. Using the data rollup timeseries  706 - 714  to calculate the virtual data points ensures that the timestamps of the input data points are synchronized and eliminates any ambiguity in which data samples should be used. 
     Referring now to  FIG. 8 , several timeseries  800 ,  820 ,  840 , and  860  illustrating the synchronization of data samples resulting from aggregating the raw timeseries data are shown, according to some embodiments. Timeseries  800  and  820  are raw data timeseries. Raw data timeseries  800  has several raw data samples  802 - 810 . Raw data sample  802  is collected at time t 1 ; raw data sample  804  is collected at time t 2 ; raw data sample  806  is collected at time t 3 ; raw data sample  808  is collected at time t 4 ; raw data sample  810  is collected at time t 5 ; and raw data sample  812  is collected at time t 6 . 
     Raw data timeseries  820  also has several raw data samples  822 ,  824 ,  826 ,  828 , and  830 . However, raw data samples,  822 - 830  are not synchronized with raw data samples  802 - 812 . For example, raw data sample  822  is collected before time t 1 ; raw data sample  824  is collected between times t 2  and t 3 ; raw data sample  826  is collected between times t 3  and t 4 ; raw data sample  828  is collected between times t 4  and t 5 ; and raw data sample  830  is collected between times t 5  and t 6 . The lack of synchronization between data samples  802 - 812  and raw data samples  822 - 830  can lead to ambiguity in which of the data samples should be used together to calculate a virtual data point. 
     Timeseries  840  and  860  are data rollup timeseries. Data rollup timeseries  840  can be generated by sample aggregator  608  by aggregating raw data timeseries  800 . Similarly, data rollup timeseries  860  can be generated by sample aggregator  608  by aggregating raw data timeseries  820 . Both raw data timeseries  800  and  820  can be aggregated using the same aggregation interval. Accordingly, the resulting data rollup timeseries  840  and  860  have synchronized data samples. For example, aggregated data sample  842  is synchronized with aggregated data sample  862  at time t 1 ′. Similarly, aggregated data sample  844  is synchronized with aggregated data sample  864  at time t 2 ′; aggregated data sample  846  is synchronized with aggregated data sample  866  at time t 3 ′; and aggregated data sample  848  is synchronized with aggregated data sample  868  at time t 4 ′. 
     The synchronization of data samples in data rollup timeseries  840  and  860  allows virtual point calculator  610  to readily identify which of the data samples should be used together to calculate a virtual point. For example, virtual point calculator  610  can identify which of the samples of data rollup timeseries  840  and  860  have the same timestamp (e.g., data samples  842  and  862 , data samples  844  and  864 , etc.). Virtual point calculator  610  can use two or more aggregated data samples with the same timestamp to calculate a timeseries value of the virtual data point. In some embodiments, virtual point calculator  610  assigns the shared timestamp of the input data samples to the timeseries value of the virtual data point calculated from the input data samples. 
     Weather Points 
     Referring again to  FIG. 6 , timeseries operators  606  are shown to include a weather point calculator  612 . Weather point calculator  612  is configured to perform weather-based calculations using the timeseries data. In some embodiments, weather point calculator  612  creates virtual data points for weather-related variables such as cooling degree days (CDD), heating degree days (HDD), cooling energy days (CED), heating energy days (HED), and normalized energy consumption. The timeseries values of the virtual data points calculated by weather point calculator  612  can be stored as derived timeseries data in local timeseries database  628  and/or hosted timeseries database  636 . 
     Weather point calculator  612  can calculate CDD by integrating the positive temperature difference between the time-varying outdoor air temperature T OA  and the cooling balance point T bC  for the building as shown in the following equation: 
       CDD=∫ period  max{0,( T   OA   −T   bC )} dt  
 
     where period is the integration period. In some embodiments, the outdoor air temperature T OA  is a measured data point, whereas the cooling balance point T bC  is a stored parameter. To calculate CDD for each sample of the outdoor air temperature T OA , weather point calculator  612  can multiply the quantity max{0, (T OA −T bC )} by the sampling period Δt of the outdoor air temperature T OA . Weather point calculator  612  can calculate CED in a similar manner using outdoor air enthalpy E OA  instead of outdoor air temperature T OA . Outdoor air enthalpy E OA  can be a measured or virtual data point. 
     Weather point calculator  612  can calculate HDD by integrating the positive temperature difference between a heating balance point T bH  for the building and the time-varying outdoor air temperature T OA  as shown in the following equation: 
       HDD=∫ period  max{0,( T   bH   −T   OA )} dt  
 
     where period is the integration period. In some embodiments, the outdoor air temperature T OA  is a measured data point, whereas the heating balance point T bH  is a stored parameter. To calculate HDD for each sample of the outdoor air temperature T OA , weather point calculator  612  can multiply the quantity max{0, (T bH −T OA )} by the sampling period Δt of the outdoor air temperature T OA . Weather point calculator  612  can calculate HED in a similar manner using outdoor air enthalpy E OA  instead of outdoor air temperature T OA . 
     In some embodiments, both virtual point calculator  610  and weather point calculator  612  calculate timeseries values of virtual data points. Weather point calculator  612  can calculate timeseries values of virtual data points that depend on weather-related variables (e.g., outdoor air temperature, outdoor air enthalpy, outdoor air humidity, outdoor light intensity, precipitation, wind speed, etc.). Virtual point calculator  610  can calculate timeseries values of virtual data points that depend on other types of variables (e.g., non-weather-related variables). Although only a few weather-related variables are described in detail here, it is contemplated that weather point calculator  612  can calculate virtual data points for any weather-related variable. The weather-related data points used by weather point calculator  612  can be received as timeseries data from various weather sensors and/or from a weather service. 
     Fault Detection 
     Still referring to  FIG. 6 , timeseries operators  606  are shown to include a fault detector  614 . Fault detector  614  can be configured to detect faults in timeseries data. In some embodiments, fault detector  614  performs fault detection for timeseries data representing meter data (e.g., measurements from a sensor) and/or for other types of timeseries data. Fault detector  614  can detect faults in the raw timeseries data and/or the derived timeseries data. In some embodiments, fault detector  614  receives fault detection rules from analytics service  524 . Fault detection rules can be defined by a user (e.g., via a rules editor) or received from an external system or device. In various embodiments, the fault detection rules can be stored within local storage  514  and/or hosted storage  516 . Fault detector  614  can retrieve the fault detection rules from local storage  514  or hosted storage  516  and can use the fault detection rules to analyze the timeseries data. 
     In some embodiments, the fault detection rules provide criteria that can be evaluated by fault detector  614  to detect faults in the timeseries data. For example, the fault detection rules can define a fault as a data value above or below a threshold value. As another example, the fault detection rules can define a fault as a data value outside a predetermined range of values. The threshold value and predetermined range of values can be based on the type of timeseries data (e.g., meter data, calculated data, etc.), the type of variable represented by the timeseries data (e.g., temperature, humidity, energy consumption, etc.), the system or device that measures or provides the timeseries data (e.g., a temperature sensor, a humidity sensor, a chiller, etc.), and/or other attributes of the timeseries data. 
     Fault detector  614  can apply the fault detection rules to the timeseries data to determine whether each sample of the timeseries data qualifies as a fault. In some embodiments, fault detector  614  generates a fault detection timeseries containing the results of the fault detection. The fault detection timeseries can include a set of timeseries values, each of which corresponds to a data sample of the timeseries data evaluated by fault detector  614 . In some embodiments, each timeseries value in the fault detection timeseries includes a timestamp and a fault detection value. The timestamp can be the same as the timestamp of the corresponding data sample of the data timeseries. The fault detection value can indicate whether the corresponding data sample of the data timeseries qualifies as a fault. For example, the fault detection value can have a value of “Fault” if a fault is detected and a value of “Not in Fault” if a fault is not detected in the corresponding data sample of the data timeseries. The fault detection timeseries can be stored in local timeseries database  628  and/or hosted timeseries database  636  along with the raw timeseries data and the derived timeseries data. 
     Referring now to  FIGS. 9A-9B , a block diagram and data table  900  illustrating the fault detection timeseries is shown, according to some embodiments. In  FIG. 9A , fault detector  614  is shown receiving a data timeseries  902  from local storage  514  or hosted storage  516 . Data timeseries  902  can be a raw data timeseries or an derived data timeseries. In some embodiments, data timeseries  902  is a timeseries of values of an actual data point (e.g., a measured temperature). In other embodiments, data timeseries  902  is a timeseries of values of a virtual data point (e.g., a calculated efficiency). As shown in table  900 , data timeseries  902  includes a set of data samples. Each data sample includes a timestamp and a value. Most of the data samples have values within the range of 65-66. However, three of the data samples have values of 42. 
     Fault detector  614  can evaluate data timeseries  902  using a set of fault detection rules to detect faults in data timeseries  902 . In some embodiments, fault detector  614  determines that the data samples having values of 42 qualify as faults according to the fault detection rules. Fault detector  614  can generate a fault detection timeseries  904  containing the results of the fault detection. As shown in table  900 , fault detection timeseries  904  includes a set of data samples. Like data timeseries  902 , each data sample of fault detection timeseries  904  includes a timestamp and a value. Most of the values of fault detection timeseries  904  are shown as “Not in Fault,” indicating that no fault was detected for the corresponding sample of data timeseries  902  (i.e., the data sample with the same timestamp). However, three of the data samples in fault detection timeseries  904  have a value of “Fault,” indicating that the corresponding sample of data timeseries  902  qualifies as a fault. As shown in  FIG. 9A , fault detector  614  can store fault detection timeseries  904  in local storage  514  (e.g., in local timeseries database  628 ) and/or hosted storage  516  (e.g., in hosted timeseries database  636 ) along with the raw timeseries data and the derived timeseries data. 
     Fault detection timeseries  904  can be used by BMS  500  to perform various fault detection, diagnostic, and/or control processes. In some embodiments, fault detection timeseries  904  is further processed by timeseries processing engine  604  to generate new timeseries derived from fault detection timeseries  904 . For example, sample aggregator  608  can use fault detection timeseries  904  to generate a fault duration timeseries. Sample aggregator  608  can aggregate multiple consecutive data samples of fault detection timeseries  904  having the same data value into a single data sample. For example, sample aggregator  608  can aggregate the first two “Not in Fault” data samples of fault detection timeseries  904  into a single data sample representing a time period during which no fault was detected. Similarly, sample aggregator  608  can aggregate the final two “Fault” data samples of fault detection timeseries  904  into a single data sample representing a time period during which a fault was detected. 
     In some embodiments, each data sample in the fault duration timeseries has a fault occurrence time and a fault duration. The fault occurrence time can be indicated by the timestamp of the data sample in the fault duration timeseries. Sample aggregator  608  can set the timestamp of each data sample in the fault duration timeseries equal to the timestamp of the first data sample in the series of data samples in fault detection timeseries  904  which were aggregated to form the aggregated data sample. For example, if sample aggregator  608  aggregates the first two “Not in Fault” data samples of fault detection timeseries  904 , sample aggregator  608  can set the timestamp of the aggregated data sample to 2015-12-31T23:10:00. Similarly, if sample aggregator  608  aggregates the final two “Fault” data samples of fault detection timeseries  904 , sample aggregator  608  can set the timestamp of the aggregated data sample to 2015-12-31T23:50:00. 
     The fault duration can be indicated by the value of the data sample in the fault duration timeseries. Sample aggregator  608  can set the value of each data sample in the fault duration timeseries equal to the duration spanned by the consecutive data samples in fault detection timeseries  904  which were aggregated to form the aggregated data sample. Sample aggregator  608  can calculate the duration spanned by multiple consecutive data samples by subtracting the timestamp of the first data sample of fault detection timeseries  904  included in the aggregation from the timestamp of the next data sample of fault detection timeseries  904  after the data samples included in the aggregation. 
     For example, if sample aggregator  608  aggregates the first two “Not in Fault” data samples of fault detection timeseries  904 , sample aggregator  608  can calculate the duration of the aggregated data sample by subtracting the timestamp 2015-12-31T23:10:00 (i.e., the timestamp of the first “Not in Fault” sample) from the timestamp 2015-12-31T23:30:00 (i.e., the timestamp of the first “Fault” sample after the consecutive “Not in Fault” samples) for an aggregated duration of twenty minutes. Similarly, if sample aggregator  608  aggregates the final two “Fault” data samples of fault detection timeseries  904 , sample aggregator  608  can calculate the duration of the aggregated data sample by subtracting the timestamp 2015-12-31T23:50:00 (i.e., the timestamp of the first “Fault” sample included in the aggregation) from the timestamp 2016-01-01T00:10:00 (i.e., the timestamp of the first “Not in Fault” sample after the consecutive “Fault” samples) for an aggregated duration of twenty minutes. 
     Eventseries 
     Referring again to  FIG. 6 , timeseries operators  606  are shown to include an eventseries generator  615 . Eventseries generator  615  can be configured to generate eventseries based on the raw data timeseries and/or the derived data timeseries. Each eventseries may include a plurality of event samples that characterize various events and define the start times and end times of the events. In the context of eventseries, an “event” can be defined as a state or condition that occurs over a period of time. In other words, an event is not an instantaneous occurrence, but rather is a non-instantaneous state or condition observed over a time period having a non-zero duration (i.e., having both a start time and a subsequent stop time). The state or condition of the event can be based on the values of the timeseries samples used to generate the eventseries. In some embodiments, eventseries generator  615  assigns a state to each timeseries sample based on the value of the timeseries sample and then aggregates multiple consecutive samples having the same state to define the time period over which that state is observed. 
     Eventseries generator  615  can be configured to assign a state to each sample of an input timeseries (e.g., a raw data timeseries or a derived timeseries) by applying a set of rules to each sample. The process of assigning a state to each sample of the input timeseries can be described as an event-condition-action (ECA) process. ECA refers to the structure of active rules in event driven architecture and active database systems. For example, each rule in the set of rules may include an event, a condition, and an action. The event part of the rule may specify a signal that triggers invocation of the rule. The condition part of the rule may be a logical test (or series of logical tests) that, if satisfied or evaluates to true, causes the action to be carried out. The action part of the rule may specify one or more actions to be performed when the corresponding logical test is satisfied (e.g., assigning a particular state to a sample of the input timeseries). 
     In some embodiments, the event part is the arrival of a new sample of an input timeseries. Different rules may apply to different input timeseries. For example, the arrival of a new sample of a first input timeseries may qualify as a first event, whereas the arrival of a new sample of a second input timeseries may qualify as a second event. Eventseries generator  615  can use the identity of the input timeseries to determine which event has occurred when a new sample of a particular input timeseries is received. In other words, eventseries generator  615  can select a particular rule to evaluate based on the identity of the input timeseries. 
     In some embodiments, the condition includes one or more mathematical checks or logic statements that are evaluated by eventseries generator  615 . For example, evaluating the condition of a particular rule may include comparing the value of the sample of the input timeseries to a threshold value. The condition may be satisfied if the value of the sample is less than the threshold value, equal to the threshold value, or greater than the threshold value, depending on the particular logic statement specified by the condition. In some embodiments, the condition includes a series of mathematical checks that are performed by eventseries generator  615  in a predetermined order. Each mathematical check may correspond to a different action to be performed if that mathematical check is satisfied. For example, the conditions and corresponding actions may be specified as follows: 
       If Value&gt;θ 1 ,Action=Action 1  
 
       Else If θ 1 ≥Value&gt;θ 2 ,Action=Action 2  
 
       Else If θ 2 ≥Value&gt;θ 3 ,Action=Action 3  
 
       Else If θ 3 ≥Value,Action=Action 4  
 
     where Value is the value of the sample of the input timeseries, θ 1 -θ 4  are thresholds for the value, and Action 1 -Action 4  are specific actions that are performed if the corresponding logic statement is satisfied. For example, Action, may be performed if the value of the sample is greater than θ 1 . 
     In some embodiments, the actions include assigning various states to the sample of the input timeseries. For example, Action 1  may include assigning a first state to the sample of the input timeseries, whereas Action 2  may include assigning a second state to the sample of the input timeseries. Accordingly, different states can be assigned to the sample based on the value of the sample relative to the threshold values. Each time a new sample of an input timeseries is received, eventseries generator  615  can run through the set of rules, select the rules that apply to that specific input timeseries, apply them in a predetermined order, determine which condition is satisfied, and assign a particular state to the sample based on which condition is satisfied. 
     One example of an eventseries which can be generated by eventseries generator  615  is an outdoor air temperature (OAT) eventseries. The OAT eventseries may define one or more temperature states and may indicate the time periods during which each of the temperature states is observed. In some embodiments, the OAT eventseries is based on a timeseries of measurements of the OAT received as a raw data timeseries. Eventseries generator  615  can use a set of rules to assign a particular temperature state (e.g., hot, warm, cool, cold) to each of the timeseries OAT samples. For example, eventseries generator  615  can apply the following set of rules to the samples of an OAT timeseries: 
       If OAT&gt;100,State=Hot 
       Else If 100≥OAT&gt;80,State=Warm
 
       Else If 80≥OAT&gt;50,State=Cool
 
       Else If 50≥OAT,State=Cold
 
     where OAT is the value of a particular timeseries data sample. If the OAT is above 100, eventseries generator  615  can assign the timeseries sample to the “Hot” temperature state. If the OAT is less than or equal to 100 and greater than 80, eventseries generator  615  can assign the timeseries sample to the “Warm” temperature state. If the OAT is less than or equal to 80 and greater than 50, eventseries generator  615  can assign the timeseries sample to the “Cool” temperature state. If the OAT is less than or equal to 50, eventseries generator  615  can assign the timeseries sample to the “Cold” temperature state. 
     In some embodiments, eventseries generator  615  creates a new timeseries that includes the assigned states for each sample of the original input timeseries. The new timeseries may be referred to as a “state timeseries” because it indicates the state assigned to each timeseries sample. The state timeseries can be created by applying the set of rules to an input timeseries as previously described. In some embodiments, the state timeseries includes a state value and a timestamp for each sample of the state timeseries. An example of a state timeseries is as follows: 
       [ state 1 ,timestamp 1   , state 2 ,timestamp 2   , . . .  state N ,timestamp N   ] 
     where state i  is the state assigned to the ith sample of the input timeseries, timestamp i  is the timestamp of the ith sample of the input timeseries, and N is the total number of samples in the input timeseries. In some instances, two or more of the state values may be the same if the same state is assigned to multiple samples of the input timeseries. 
     In some embodiments, the state timeseries also includes the original value of each sample of the input timeseries. For example, each sample of the state timeseries may include a state value, a timestamp, and an input data value, as shown in the following equation: 
       [ state 1 ,timestamp 1 ,value 1   , . . .  state N ,timestamp N ,value N   ] 
     where value i  is the original value of the ith sample of the input timeseries. The state timeseries is a type of derived timeseries which can be stored and processed by timeseries service  528 . 
     Referring now to  FIG. 9C , a table  910  illustrating the result of assigning a temperature state to each timeseries sample is shown, according to some embodiments. Each timeseries sample is shown as a separate row of table  910 . The “Time” column of table  910  indicates the timestamp associated with each sample, whereas the “OAT” column of table  910  indicates the value of each timeseries sample. The “State” column of table  910  indicates the state assigned to each timeseries sample by eventseries generator  615 . 
     Referring now to  FIG. 9D , a table  920  illustrating a set of events generated by eventseries generator  615  is shown, according to some embodiments. Each event is shown as a separate row of table  920 . The “Event ID” column of table  920  indicates the unique identifier for each event (e.g., Event 1, Event 2, etc.). The “Start Time” column of table  920  indicates the time at which each event begins and the “End Time” column of table  920  indicates the time at which event ends. The “State” column of table  920  indicates the state associated with each event. 
     Eventseries generator  615  can generate each event shown in table  920  by identifying consecutive timeseries samples with the same assigned state and determining a time period that includes the identified samples. In some embodiments, the time period starts at the timestamp of the first sample having a given state and ends immediately before the timestamp of the next sample having a different state. For example, the first two timeseries samples shown in table  910  both have the state “Cold,” whereas the third sample in table  910  has the state “Cool.” Eventseries generator  615  can identify the first two samples as having the same state and can generate the time period 00:00-01:59 which includes both of the identified samples. This time period begins at the timestamp of the first sample (i.e., 00:00) and ends immediately before the timestamp of the third sample (i.e., 02:00). Eventseries generator  615  can create an event for each group of consecutive samples having the same state. 
     Eventseries generator  615  can perform a similar analysis for the remaining timeseries samples in table  910  to generate each of the events shown in table  920 . In some instances, multiple events can have the same state associated therewith. For example, both Event 1 and Event 7 shown in table  920  have the “Cold” state. Similarly, both Event 2 and Event 6 have the “Cool” state and both Event 3 and Event 5 have the “Warm” state. It should be noted that an event defines not only a particular state, but also a time period (i.e., a series of consecutive time samples) during which that state is observed. If the same state is observed during multiple non-consecutive time periods, multiple events having the same state can be generated to represent each of the non-consecutive time periods. 
     In some embodiments, eventseries generator  615  creates an eventseries for a set of events. An eventseries is conceptually similar to a timeseries in that both represent a series of occurrences. However, the samples of a timeseries correspond to instantaneous occurrences having a single timestamp, whereas the samples of an eventseries correspond to non-instantaneous events having both a start time and a stop time. For example, eventseries generator  615  may create the following eventseries for the set of events shown in table  920 : 
       [ ID=1,State=Cold,StartTime=00:00,EndTime=01;59 , ID=2,State=Cool,StartTime=02:00,EndTime=08;59 , ID=3,State=Warm,StartTime=09:00,EndTime=11;59 , ID=4,State=Hot,StartTime=12:00,EndTime=15;59 , ID=5,State=Warm,StartTime=16:00,EndTime=18;59 , ID=6,State=Cool,StartTime=19:00,EndTime=21;59 , ID=7,State=Cold,StartTime=22:00,EndTime=23;59 ,] 
     where each item within the bent brackets     is an event having the attributes ID, State, StartTime, and EndTime. Events can be stored in a tabular format (as shown in  FIG. 9D ), as a text string (as shown above), as a data object (e.g., a JSON object), in a container format, or any of a variety of formats. 
     Eventseries Updates—Streaming Data 
     Table  920  shown in  FIG. 9D  represents the final set of events for the time period ranging from 00:00-23:59. In some embodiments, the events in table  920  are generated after all of the timeseries samples within the time period have been collected. However, eventseries generator  615  can also generate and update events in real time as the data samples are collected. This functionality allows eventseries generator  615  to update events and/or eventseries in real time upon receiving individual samples of incoming streaming data. 
     Referring now to  FIGS. 9E-9H , several tables illustrating how eventseries generator  615  can update an eventseries in real time upon receiving new samples of streaming data are shown, according to some embodiments.  FIG. 9E  shows table  910  broken into five segments. The top segment includes all of the data samples received up to time t 1  and identifies the state associated with each data sample. At time t 1 , eventseries generator  615  can translate the information in table  910  into table  921  shown in  FIG. 9F . At time t 1 , the most recent data sample (i.e., the sample with timestamp 15:00) was associated with the “Hot” temperature state, which indicates that the “Hot” temperature state is still active. The end time of the “Hot” temperature state cannot be determined based on the information known at time t 1 . Accordingly, table  921  is shown to include a value of “Null” as the end time of Event 4. 
     At time t 2 , eventseries generator  615  receives the next sample of the OAT timeseries. This sample has a timestamp of 16:00 and is associated with the “Warm” state. At time t 2 , eventseries generator  615  can determine that the “Hot” state is no longer active and the system has transitioned into the “Warm” state. Accordingly, eventseries generator  615  can update table  921  to create table  922  shown in  FIG. 9G . In table  922 , the “Null” value at the end time of Event 4 is updated with the actual end time of Event 4 (i.e., 15:59). Eventseries generator  615  can also add a new event (i.e., Event 5) to table  922  to represent the new event associated with the current “Warm” state. Event 5 has a start time of 16:00 and an end time of “Null” since the actual end time of Event 5 is unknown given the information known at time t 2 . 
     At times t 3  and t 4 , eventseries generator  615  receives the next two samples of the OAT timeseries. These samples have timestamps of 17:00 and 18:00 and both are associated with the “Warm” state. Eventseries generator  615  does not need to update table  922  at times t 3  and t 4  since the new samples indicate that Event 5 is still active and has not yet ended. Accordingly, the end time of Event 5 remains “Null” and the “Warm” state is still the most recent state. 
     At time t 5 , eventseries generator  615  receives the next sample of the OAT timeseries. This sample has a timestamp of 19:00 and is associated with the “Cool” state. At time t 5 , eventseries generator  615  can determine that the “Warm” state is no longer active and the system has transitioned into the “Cool” state. Accordingly, eventseries generator  615  can update table  922  to create table  925  shown in  FIG. 9H . In table  925 , the “Null” value at the end time of Event 5 is updated with the actual end time of Event 5 (i.e., 18:59). Eventseries generator  615  can also add a new event (i.e., Event 6) to table  925  to represent the new event associated with the current “Cool” state. Event 6 has a start time of 19:00 and an end time of “Null” since the actual end time of Event 6 is unknown given the information known at time t 5 . 
     Eventseries Updates—Out of Order Data 
     The above scenario assumes that each incoming sample of the timeseries data is received in the correct order (i.e., with monotonically increasing timestamps). However, eventseries generator  615  can also be configured to update events and eventseries if the incoming samples are received out of order. The following scenarios illustrate how eventseries generator  615  can handle out of order data. 
     Scenario A 
     Referring now to  FIGS. 9I-9M , several tables illustrating how eventseries generator  615  can update an eventseries in real time when incoming data samples are received out of order are shown, according to some embodiments. In this scenario, the data sample having timestamp 16:00 is received after the data sample having timestamp 17:00.  FIG. 9I  shows table  910  broken into five segments. The top segment includes all of the data samples received up to time t 1  and identifies the state associated with each data sample. At time t 1 , eventseries generator  615  can translate the information in table  910  into table  931  shown in  FIG. 9J . At time t 1 , the most recent data sample (i.e., the sample with timestamp 15:00) was associated with the “Hot” temperature state, which indicates that the “Hot” temperature state is still active. The end time of the “Hot” temperature state cannot be determined based on the information known at time t 1 . Accordingly, table  931  is shown to include a value of “Null” as the end time of Event 4. 
     At time t 2 , eventseries generator  615  receives another sample of the OAT timeseries. This sample has a timestamp of 17:00 and is associated with the “Warm” state. At time t 2 , eventseries generator  615  can determine that the “Hot” state is no longer active and the system has transitioned into the “Warm” state. Accordingly, eventseries generator  615  can update table  931  to create table  932  shown in  FIG. 9K . In table  932 , the “Null” value at the end time of Event 4 is updated with the estimated end time of Event 4 (i.e., 16:59). It should be noted that this end time is not the actual end time, but rather the best estimate given the information known up to time t 2 . The actual end time of Event 4 may have occurred anytime between timestamp 15:00 and timestamp 17:00. Eventseries generator  615  can also add a new event (i.e., Event 5) to table  932  to represent the new event associated with the current “Warm” state. Event 5 has a start time of 17:00 and an end time of “Null” since the actual end time of Event 5 is unknown given the information known at time t 2 . 
     At time t 3 , eventseries generator  615  receives another sample of the OAT timeseries. This sample has a timestamp of 16:00 and is associated with the “Warm” state. At time t 3 , eventseries generator  615  can determine that the estimated end time of Event 4 (i.e., 16:59) and the start time of Event 5 need to be updated based on the information provided by the sample received at time t 3 . Specifically, eventseries generator  615  can update the end time of Event 4 to 15:59 and can update the start time of Event 5 to 16:00, as shown in table  933  in  FIG. 9L . Since the end time of Event 5 cannot be determined based on the information known at time t 3 , the end time of Event 5 may remain “Null.” 
     At time t 4 , eventseries generator  615  receives the next sample of the OAT timeseries. This sample has a timestamp of 18:00 and is associated with the “Warm” state. Eventseries generator  615  does not need to update table  933  at time t 4  since the new samples indicate that Event 5 is still active and has not yet ended. Accordingly, the end time of Event 5 remains “Null” and the “Warm” state is still the most recent state. 
     At time t 5 , eventseries generator  615  receives the next sample of the OAT timeseries. This sample has a timestamp of 19:00 and is associated with the “Cool” state. At time t 5 , eventseries generator  615  can determine that the “Warm” state is no longer active and the system has transitioned into the “Cool” state. Accordingly, eventseries generator  615  can update table  933  to create table  935  shown in  FIG. 9H . In table  923 , the “Null” value at the end time of Event 5 is updated with the actual end time of Event 5 (i.e., 18:59). Eventseries generator  615  can also add a new event (i.e., Event 6) to table  935  to represent the new event associated with the current “Cool” state. Event 6 has a start time of 19:00 and an end time of “Null” since the actual end time of Event 6 is unknown given the information known at time t 5 . 
     Scenario B 
     Referring now to  FIGS. 9N-9R , several tables illustrating another example of how eventseries generator  615  can update an eventseries in real time when incoming data samples are received out of order are shown, according to some embodiments. In this scenario, the data sample having timestamp 16:00 is received after the data sample having timestamp 19:00.  FIG. 9N  shows table  910  broken into five segments. The top segment includes all of the data samples received up to time t 1  and identifies the state associated with each data sample. At time t 1 , eventseries generator  615  can translate the information in table  910  into table  941  shown in  FIG. 9O . At time t 1 , the most recent data sample (i.e., the sample with timestamp 15:00) was associated with the “Hot” temperature state, which indicates that the “Hot” temperature state is still active. The end time of the “Hot” temperature state cannot be determined based on the information known at time t 1 . Accordingly, table  941  is shown to include a value of “Null” as the end time of Event 4. 
     At time t 2 , eventseries generator  615  receives another sample of the OAT timeseries. This sample has a timestamp of 17:00 and is associated with the “Warm” state. At time t 2 , eventseries generator  615  can determine that the “Hot” state is no longer active and the system has transitioned into the “Warm” state. Accordingly, eventseries generator  615  can update table  941  to create table  942  shown in  FIG. 9P . In table  942 , the “Null” value at the end time of Event 4 is updated with the estimated end time of Event 4 (i.e.,  16 : 59 ). It should be noted that this end time is not the actual end time, but rather the best estimate given the information known up to time t 2 . The actual end time of Event 4 may have occurred anytime between timestamp 15:00 and timestamp 17:00. Eventseries generator  615  can also add a new event (i.e., Event 5) to table  942  to represent the new event associated with the current “Warm” state. Event 5 has a start time of 17:00 and an end time of “Null” since the actual end time of Event 5 is unknown given the information known at time t 2 . 
     At time t 3 , eventseries generator  615  receives the next sample of the OAT timeseries. This sample has a timestamp of 18:00 and is associated with the “Warm” state. Eventseries generator  615  does not need to update table  942  at time t 3  since the new samples indicate that Event 5 is still active and has not yet ended. Accordingly, the end time of Event 5 remains “Null” and the “Warm” state is still the most recent state. 
     At time t 4 , eventseries generator  615  receives the next sample of the OAT timeseries. This sample has a timestamp of 19:00 and is associated with the “Cool” state. At time t 4 , eventseries generator  615  can determine that the “Warm” state is no longer active and the system has transitioned into the “Cool” state. Accordingly, eventseries generator  615  can update table  942  to create table  944  shown in  FIG. 9Q . In table  944 , the “Null” value at the end time of Event 5 is updated with the actual end time of Event 5 (i.e., 18:59). Eventseries generator  615  can also add a new event (i.e., Event 6) to table  944  to represent the new event associated with the current “Cool” state. Event 6 has a start time of 19:00 and an end time of “Null” since the actual end time of Event 6 is unknown given the information known at time t 4 . 
     At time t 5 , eventseries generator  615  receives another sample of the OAT timeseries. This sample has a timestamp of 16:00 and is associated with the “Warm” state. At time t 4 , eventseries generator  615  can determine that the estimated end time of Event 4 (i.e., 16:59) and the start time of Event 5 need to be updated based on the information provided by the sample received at time t 3 . Specifically, eventseries generator  615  can update the end time of Event 4 to 15:59 and can update the start time of Event 5 to 16:00, as shown in table  945  in  FIG. 9R . Since the end time of Event 6 cannot be determined based on the information known at time t 5 , the end time of Event 6 may remain “Null.” 
     Scenario C 
     Referring now to  FIGS. 9S-9Y , several tables illustrating another example of how eventseries generator  615  can update an eventseries in real time when incoming data samples are received out of order are shown, according to some embodiments. In this scenario, the data samples are received in the order shown in  FIGS. 9S and 9W . The data samples with timestamps 00:00-11:00 are received in the correct order. However, the next three samples received have timestamps 17:00, 18:00, and 19:00. The next sample received has timestamp 15:00, followed by the samples with timestamps 12:00 and 13:00. The final two samples received have timestamps 16:00 and 14:00. 
       FIG. 9S  shows table  910  broken into five segments. The top segment includes all of the data samples received up to time t 1  and identifies the state associated with each data sample. At time t 1 , eventseries generator  615  can translate the information in table  910  into table  951  shown in  FIG. 9T . At time t 1 , the most recent data sample (i.e., the sample with timestamp 11:00) was associated with the “Warm” temperature state, which indicates that the “Warm” temperature state is still active. The end time of the “Warm” temperature state cannot be determined based on the information known at time t 1 . Accordingly, table  951  is shown to include a value of “Null” as the end time of Event 3. 
     At time t 2 , eventseries generator  615  receives another sample of the OAT timeseries. This sample has a timestamp of 17:00 and is associated with the “Warm” state. Although a complete picture of the timeseries data would show that the system has transitioned into the “Hot” state and then back into the “Warm” state, the information received up to time t 2  indicates (incorrectly) that the system has remained in the “Warm” state from 11:00 to 17:00. Accordingly, eventseries generator  615  determines that the system is still in the “Warm” state at time t 2  and does not update table  951 . The sample received with timestamp 18:00 also indicates that the system is still in the “Warm” state and does not trigger an update. 
     At time t 3 , eventseries generator  615  receives another sample of the OAT timeseries. This sample has a timestamp of 19:00 and is associated with the “Cool” state. At time t 3 , eventseries generator  615  can determine that the “Warm” state is no longer active and the system has transitioned into the “Cool” state. Accordingly, eventseries generator  615  can update table  951  to create table  953  shown in  FIG. 9U . In table  953 , the “Null” value at the end time of Event 3 is updated with the estimated end time of Event 3 (i.e., 18:59). This end time is not the actual end time, but rather the best estimate given the information known up to time t 3 . The actual end time of Event 3 may have occurred anytime between timestamp 09:00 and timestamp 19:00. Eventseries generator  615  can also add a new event (i.e., Event 4) to table  953  to represent the new event associated with the current “Cool” state. Event 4 has a start time of 19:00 and an end time of “Null” since the actual end time of Event 4 is unknown given the information known at time t 3 . 
     At time t 4 , eventseries generator  615  receives the next sample of the OAT timeseries. This sample has a timestamp of 15:00 and is associated with the “Hot” state. At time t 4 , eventseries generator  615  can determine that the time period associated with Event 3 is actually three separate events (i.e., two “Warm” events with a “Hot” event in between). Accordingly, eventseries generator  615  can update table  953  to create table  954  shown in  FIG. 9V . In table  954 , the end time of Event 3 is updated to 14:59 and a new event (i.e., Event 5) is added to represent the time period during which the “Hot” state was active. Event 5 has a start time of 15:00 and an end time of 16:59. Another new event (i.e., Event 6) is added to represent the second “Warm” time period which was previously part of Event 3. Event 6 has a start time of 17:00 and an end time of 18:59. The events shown in table  954  are arranged in temporal order rather than in the order of the event ID. 
     At time t 5 , eventseries generator  615  receives another sample of the OAT timeseries. This sample has a timestamp of 12:00 and is associated with the “Hot” state. At time t 5 , eventseries generator  615  can determine that the estimated end time of Event 3 (i.e., 14:59) and the estimated start time of Event 5 (i.e., 15:00) need to be updated based on the information provided by the sample received at time t 5 . Specifically, eventseries generator  615  can update the end time of Event 3 to 11:59 and can update the start time of Event 5 to 12:00, as shown in table  955  in  FIG. 9X . Since the end time of Event 4 cannot be determined based on the information known at time t 5 , the end time of Event 6 may remain “Null.” 
     At time t 6 , eventseries generator  615  receives another sample of the OAT timeseries. This sample has a timestamp of 16:00 and is associated with the “Warm” state. At time t 6 , eventseries generator  615  can determine that the estimated end time of Event 5 (i.e., 16:59) and the estimated start time of Event 6 (i.e., 16:00) need to be updated based on the information provided by the sample received at time t 6 . Specifically, eventseries generator  615  can update the end time of Event 5 to 15:59 and can update the start time of Event 6 to 16:00, as shown in table  956  in  FIG. 9Y . Since the end time of Event 4 still cannot be determined based on the information known at time t 6 , the end time of Event 6 may remain “Null.” 
     Eventseries Process 
     Referring now to  FIG. 9Z , a flowchart of a process  960  for creating and updating eventseries is shown, according to some embodiments. Process  960  can be performed by eventseries generator  615 , as described with reference to  FIGS. 6 and 9C-9Y . In some embodiments, process  960  is performed to create an eventseries based on the samples of a data timeseries. Process  960  can be performed after all of the samples of the data timeseries have been collected or can be performed each time a new sample of the data timeseries is collected. 
     Process  960  is shown to include obtaining a new sample of a data timeseries (step  962 ) and assigning a state to the sample using a set of rules (step  964 ). In some embodiments, the sample is obtained from a sensor configured to measure a variable of interest in or around a building. For example, the sample can be a sample of a raw data timeseries. In other embodiments, the sample is a sample of a derived data timeseries generated by sample aggregator  608 , virtual point calculator  610 , weather point calculator  612 , or other timeseries operators  606 . The sample can be obtained from a set of samples of a complete timeseries or can be received as the latest sample of an incoming data stream. 
     In some embodiments, step  964  includes applying a set of rules to the sample of the data timeseries to determine which state to assign. The set of rules may define various ranges of values and a corresponding state for each range of values. Step  964  can include assigning the sample to a particular state if the value of the value of the sample is within the corresponding range of values. For example, if the sample is a sample of outdoor air temperature (OAT), the set of rules may define various temperature ranges and a temperature state for each of the temperature ranges. One example of such a set of rules is as follows: 
       If OAT&gt;100,State=Hot 
       Else If 100≥OAT&gt;80,State=Warm
 
       Else If 80≥OAT&gt;50,State=Cool
 
       Else If 50≥OAT,State=Cold
 
     where OAT is the value of a particular timeseries data sample. If the OAT is above 100, the sample can be assigned to the “Hot” temperature state. If the OAT is less than or equal to 100 and greater than 80, the sample can be assigned to the “Warm” temperature state. If the OAT is less than or equal to 80 and greater than 50, the sample can be assigned to the “Cool” temperature state. If the OAT is less than or equal to 50, the sample can be assigned to the “Cold” temperature state. 
     Still referring to  FIG. 9Z , process  960  is shown to include determining whether the sample is part of an existing event (step  966 ). Step  966  may include identifying all of the events in an existing eventseries and determining whether the sample belongs to any of the identified events. Each event may be defined by the combination of a particular state and a time period having both a start time and an end time. Step  966  may include determining that the sample is part of an existing event if the sample is both (1) assigned to the same state as the existing event and (2) has a timestamp that is either (a) within the time period associated with the existing event or (b) consecutive with the time period associated with the existing event. However, step  966  may include determining that the sample is not part of an existing event if the sample does not have the same state as the existing event or does not have a timestamp that that is either within the time period associated with the existing event or consecutive with the time period associated with the existing event. 
     In step  966 , a timestamp may be considered within the time period associated with an existing event if the timestamp is between the start time of the event and the end time of the event. A timestamp may be considered consecutive with the time period associated with an existing event if the timestamp is immediately before the start time or immediately after the end time of the event. For example, if a new sample has a timestamp before the start time of an event and no other samples have intervening timestamps between the timestamp of the new sample and the start time of the event, the timestamp may be considered consecutive with the time period associated with the existing event. Similarly, if a new sample has a timestamp after the end time of an event and no other samples have intervening timestamps between the end time of the event and the timestamp of the new sample, the timestamp may be considered consecutive with the time period associated with the existing event. 
     If the new sample is part of an existing event (i.e., the result of step  966  is “yes”), process  960  may proceed to determining whether the new sample extends the existing event (step  968 ). Step  968  may include determining whether the timestamp of the new sample is consecutive with the time period associated with the existing event (i.e., immediately before the start time of the event or immediately after the end time of the event). If the timestamp of the new sample is consecutive with the time period associated with the existing event, step  968  may include determining that the sample extends the existing event. However, if the timestamp of the new sample is not consecutive with the time period associated with the existing event, step  968  may include determining that the sample does not extend the existing event. 
     If the sample does not extend the existing event (i.e., the result of step  968  is “no”), process  960  may include determining that no update to the existing event is needed (step  970 ). This situation may occur when the timestamp of the new sample is between the start time of the existing event and the end time of the existing event (i.e., within the time period associated with the existing event). Since the time period associated with the existing event already covers the timestamp of the new sample, it may be unnecessary to update the existing event to include the timestamp of the new sample. 
     However, if the sample extends the existing event (i.e., the result of step  968  is “yes”), process  960  may proceed to updating the start time or end time of the existing event based on the timestamp of the sample (step  972 ). Step  972  may include moving the start time of the event backward in time or moving the end time of the event forward in time such that the time period between the start time and the end time includes the timestamp of the new sample. For example, if the timestamp of the sample is before the start time of the event, step  972  may include replacing the start time of the existing event with the timestamp of the sample. 
     Similarly, if the timestamp of the sample is after the end time of the event, step  972  may include replacing the end time of the existing event with a new end time that occurs after the timestamp of the sample. For example, if the existing event has an original end time of 04:59 and the new sample has a timestamp of 05:00, step  972  may include updating the end time of the event to 05:59 (or any other time that occurs after 05:00) such that the adjusted time period associated with the event includes the timestamp of the new sample. If the original end time of the existing event is “Null” and the new sample extends the end time of the existing event, step  972  may maintain the original end time of “Null.” This situation is described in greater detail with reference to  FIGS. 9E-9H . 
     Returning to step  966 , if the sample is not part of an existing event (i.e., the result of step  966  is “no”), process  960  may proceed to creating a new event based on the state and the timestamp of the new sample (step  974 ). The new event may have a state that matches the state assigned to the new sample in step  964 . The new event may have a start time equal to the timestamp of the sample and an end time that occurs after the timestamp of the sample such that the time period associated with the new event includes the timestamp of the sample. The end time may have a value of “Null” if the new event is the last event in the eventseries or a non-null value of the new event is not the last event in the eventseries. For example, if the next event in the timeseries begins at timestamp 06:00, step  974  may include setting the end time of the new event to 05:59. 
     After creating the new event in step  974 , process  960  may perform steps  976 - 988  to update other events in the eventseries based on the new information provided by the new event. For example, if the new event is the last event in the eventseries (i.e., the result of step  976  is “yes”), process  960  may update the end time of the previous event (i.e., the event that occurs immediately before the new event) (step  978 ). The update performed in step  978  may include setting the end time of the previous event to a time immediately before the timestamp of the new sample. For example, if the new sample has a timestamp of 05:00, step  978  may include updating the end time of the previous event to 04:59. If the new event is not the last event in the eventseries (i.e., the result of step  976  is “no”), process  960  may proceed to step  980 . 
     If the new event occurs between existing events in the eventseries (i.e., the result of step  980  is “yes”), process  960  may update the end time of the previous event (step  982 ). The update performed in step  982  may be the same as the update performed in step  978 . For example, the update performed in step  982  may include setting the end time of the previous event to a time immediately before the timestamp of the new sample. If the new event does not occur between existing events in the eventseries (i.e., the result of step  980  is “no”), process  960  may proceed to step  984 . 
     If the new event splits an existing event in the eventseries (i.e., the result of step  984  is “yes”), process  960  may split the existing event into two events with the new event in between. In some embodiments, splitting the existing event into two events includes updating the end time of the existing event to end before the new event (step  986 ) and creating a second new event beginning after the first new event and ending at the previous end time of the existing event (step  988 ). For example, consider a situation in which the existing event has a start time of 04:00, an end time of 11:59, and a state of “Warm.” The new event added in step  974  may have a start time of 08:00, an end time of 08:59, and a state of “Hot.” Accordingly, step  986  may include changing the end time of the existing event to 07:59 such that the existing event corresponds to a first “Warm” event and covers the time period from 04:00 to 07:59. The intervening “Hot” event may cover the time period from 08:00 to 08:59. The second new event created in step  988  (i.e., the second “Warm” event) may have a start time of 09:00 and an end time of 11:59. The state of the second new event may be the same as the state of the existing event. 
     Properties of Events and Eventseries 
     Similar to timeseries, an eventseries can be used in two ways. In some embodiments, an event series is used for storage only. For example, events can be created by an external application and stored in an eventseries. In this scenario, the eventseries is used only as a storage container. In other embodiments, eventseries can be used for both storage and processing. For example, events can be created by eventseries generator  615  based on raw or derived timeseries by applying a set of rules, as previously described. In this scenario, the eventseries is both the storage container and the mechanism for creating the events. 
     In some embodiments, each eventseries includes the following properties or attributes: EventseriesID, OrgID, InputTimeseriesID, StateTimeseriesID, Rules, and Status. The EventseriesID property may be a unique ID generated by eventseries generator  615  when a new eventseries is created. The EventseriesID property can be used to uniquely identify the eventseries and distinguish the eventseries from other eventseries. The OrgID property may identify the organization (e.g., “ABC Corporation”) to which the eventseries belongs. Similar to timeseries, each eventseries may belong to a particular organization, building, facility, or other entity (described in greater detail with reference to  FIGS. 11A-11B ). 
     The InputTimeseriesID property may identify the timeseries used to create the eventseries. For example, if the eventseries is a series of outdoor air temperature (OAT) events, the InputTimeseriesID property may identify the OAT timeseries from which the OAT eventseries is generated. In some embodiments, the input timeseries has the following format: 
       [&lt;key,timestamp 1 ,value 1 &gt;,&lt;key,timestamp 2 ,value 2 &gt;,&lt;key,timestamp 3 ,value 3 &gt;] 
     where key is an identifier of the source of the data samples (e.g., timeseries ID, sensor ID, etc.), timestamp i  identifies a time associated with the ith sample, and value r  indicates the value of the ith sample. 
     The Rules property may identify a list of rules that are applied to the input timeseries to assign a particular state to each sample of the input timeseries. In some embodiments, the list of rules includes a plurality of rules that are applied in a particular order. The order may be defined by the logical structure of the rules. For example, the rules may include a set of “If” and “Elself” statements that are evaluated in the order in which the statements appear in the set of rules. An example of a set of rules is as follows: 
       If OAT&gt;100,State=Hot 
       Else If 100≥OAT&gt;80,State=Warm
 
       Else If 80≥OAT&gt;50,State=Cool
 
       Else If 50≥OAT,State=Cold
 
     The StateTimeseriesID property may identify the state timeseries in which the assigned states are stored. The state timeseries can be created by applying the set of rules to an input timeseries as previously described. In some embodiments, the state timeseries includes a state value and a timestamp for each sample of the state timeseries. An example of a state timeseries is as follows: 
       [ state 1 ,timestamp 1   , state 2 ,timestamp 2   , . . .  state 5 ,timestamp 5   ] 
     where state i  is the state assigned to the ith sample of the input timeseries, timestamp i  is the timestamp of the ith sample of the input timeseries, and N is the total number of samples in the input timeseries. 
     The Status property may indicate whether the eventseries is active (i.e., Status=Active) or inactive (i.e., Status=Inactive). In some embodiments, an eventseries is active by default when the eventseries is created. An eventseries can be deactivated by events service  603 . Events service  603  can change the Status property from active to inactive upon deactivating an eventseries. 
     Each eventseries may include a set of events. Each event may include the following properties: EventID, State, StartTimestamp, EndTimestamp, and EventseriesID. The EventID property may be a unique ID generated by eventseries generator  615  when a new event is created. The EventID property can be used to uniquely identify a particular event and distinguish the event from other events in the eventseries. The State property may be a text string that defines the state associated with the event. Each event may be uniquely associated with one state. The StartTimestamp property may indicate the start time of the event, whereas the EndTimestamp property may indicate the end time of the event. The StartTimestamp and EndTimestamp properties may be timestamps in any of a variety of formats (e.g., 2017-01-01T00:00:00). The EventseriesID property may identify the eventseries which includes the event. The EventseriesID property may be the same unique identifier used to identify and distinguish eventseries from each other. 
     Event Service 
     Referring again to  FIG. 6 , timeseries service  528  is shown to include an event service  603 . In some embodiments, event service  603  is part of timeseries service  528 . In other embodiments, event service  603  is a separate service (i.e., separate from timeseries service  528 ) within data platform services  520 . Event service  603  can be configured to receive and process requests for information relating to various events and eventseries. Event service  603  can also create and update events and eventseries in response to a request from an application or a user. Several examples of how event service  603  can handle requests are described below. The following table identifies the types of actions event service  603  can perform with respect to events and eventseries: 
     
       
         
           
               
               
               
               
             
               
                   
               
               
                 Resource 
                 GET (read) 
                 POST (create) 
                 PUT (update) 
               
               
                   
               
             
            
               
                 /Eventseries 
                 Retrieve list of 
                 Create one or more 
                 N/A 
               
               
                   
                 Eventseries 
                 new Eventseries 
                   
               
               
                 /Eventseries/ 
                 Read a specific 
                 Create a specific 
                 Update the specific 
               
               
                 {eventseriesId} 
                 Eventseries 
                 Eventseries 
                 Eventseries 
               
               
                 /Events 
                 Retrieve a list of  
                 Create one or more 
                 N/A 
               
               
                   
                 Events 
                 new Events 
                   
               
               
                 /Events/ 
                 Read a specific 
                 Create a specific 
                 Update the specific 
               
               
                 {eventId} 
                 Event 
                 Event 
                 Event 
               
               
                   
               
            
           
         
       
     
     Event service  603  can be configured to create a new eventseries in response to a request containing an OrgID attribute and a processing type attribute. For example, event service  603  can receive the following request: 
                                                    Post {timeseriesV2}/eventseries/new               {                  “orgId”: “Abc Inc”,                  “ProcessingType” : “none”               }                        
where “Abc Inc” is the ID of the organization to which the new eventseries will belong and no processing type is specified.
 
     In response to this request, event service  603  can create a new eventseries (i.e., an empty eventseries container) and assign an EventseriesID to the eventseries. For example, event service  603  can respond to the request as follows: 
     
       
         
           
               
               
             
               
                   
               
             
            
               
                   
                 { 
               
               
                   
                    “eventseriesId”: “c7c157e4-603f-4b25-b182-ce7b0f8291d8”, 
               
               
                   
                    “orgId”: “Abc Inc”, 
               
               
                   
                    “inputTimeseriesId”: null, 
               
               
                   
                    “stateTimeseriesId”: null, 
               
               
                   
                    “rules”: null, 
               
               
                   
                    “status”: “active”, 
               
               
                   
                    “processingType”: “stream” 
               
               
                   
                 } 
               
               
                   
               
            
           
         
       
     
     In some embodiments, event service  603  is configured to create a new eventseries in response to a request containing an OrgID attribute, an InputTimeseriesID attribute, a StateTimeseriesID attribute, and a Rules attribute. For example, event service  603  can receive the following request: 
                                {          “orgId”: “Abc Inc”,          “inputTimeseriesId”: “793c156e4-603f-4b2e-bt82-ce7b0f829uj3”,          “stateTimeseriesId”: “uic157e4-6r2f-4b25-b682-ct7b0f82917u”,          “rules”: [           {“compareOp”: “Gt”, “scalar”: 100, “state”: “Hot”},           {“compareOp”: “Gt”, “scalar”: 80, “state”: “Warm”},           {“compareOp”: “Gt”, “scalar”: 50, “state”: “Cool”},           {“compareOp”: “Lte”, “scalar”: 50, “state”: “Cold”}          ]       }                    
where “793c156e4-603f-4b2e-bt82-ce7b0f829uj3” is the ID of the input timeseries used to generate the eventseries, “uic157e4-6r2f-4b25-b682-ct7b0f82917u” is the ID of the state timeseries containing the states assigned to each sample of the input timeseries, and the “rules” attribute contains a set of rules used to assign a state to each sample of the input timeseries.
 
     In response to this request, event service  603  can create a new eventseries (i.e., an empty eventseries container) and assign an EventseriesID to the eventseries. For example, event service  603  can respond to the request as follows: 
     
       
         
           
               
             
               
                   
               
             
            
               
                 { 
               
               
                    “eventseriesId”: “c7c157e4-603f-4b25-b182-ce7b0f8291d8”, 
               
               
                    “orgId”: “Abc Inc”, 
               
               
                    “inputTimeseriesId”: “793c156e4-603f-4b2e-bt82-ce7b0f829uj3”, 
               
               
                    “stateTimeseriesId”: “uic157e4-6r2f-4b25-b682-ct7b0f82917u”, 
               
               
                    “rules”: [ 
               
               
                     {“compareOp”: “Gt”, “scalar”: 100, “state”: “Hot”}, 
               
               
                     {“compareOp”: “Gt”, “scalar”: 80, “state”: “Warm”}, 
               
               
                     {“compareOp”: “Gt”, “scalar”: 50, “state”: “Cool”}, 
               
               
                     {“compareOp”: “Lte”, “scalar”: 50, “state”: “Cold”} 
               
               
                    ], 
               
               
                    “status”: “active”, 
               
               
                    “processingType”: “stream” 
               
               
                 } 
               
               
                   
               
            
           
         
       
     
     In some embodiments, event service  603  is configured to add new events to an existing eventseries. For example, event service  603  can receive a request to add a new event to an eventseries. The request may specify the EventseriesID, the start time of the event, the end time of the event, and the state associated with the event, as shown in the following request: 
     
       
         
           
               
             
               
                   
               
             
            
               
                 Post {timeseriesV2}/eventseries/c7c157e4-603f-4b25-b182-ce7b0f8291d8/events 
               
               
                 [ 
               
               
                   { 
               
               
                    “eventseriesId”: “c7c157e4-603f-4b25-b182-ce7b0f8291d8”, 
               
               
                    “startTimestamp”: “2017-04-01 13:48:23-05:00”, 
               
               
                    “endTimestamp”: “2017-04-01 13:54:11-05:00”, 
               
               
                    “state”: “High Pressure Alarm” 
               
               
                   } 
               
               
                 ] 
               
               
                   
               
            
           
         
       
     
     In response to this request, event service  603  can generate a new EventID for the new event and can add the new event to the eventseries designated by the EventseriesID “c7c157e4-603f-4b25-b182-ce7b0f8291d8.” The new event may have the start time “2017-04-01 13:48:23-05:00,” the end time “2017-04-01 13:54:11-05:00,” and the state “High Pressure Alarm” as specified in the request. In some embodiments, event service  603  responds to the request by acknowledging that the new event has been added to the eventseries. 
     In some embodiments, event service  603  is configured to update existing events in an eventseries. For example, event service  603  can receive a request to add update one or more properties of an existing event in an eventseries. The request may specify the EventseriesID, the updated start time of the event, the updated end time of the event, and/or the updated state associated with the event, as shown in the following request: 
     
       
         
           
               
             
               
                   
               
             
            
               
                 Put {timeseriesV2}/eventseries/c7c157e4-603f-4b25-b182-ce7b0f8291d8/events/ 
               
               
                 c7c157e4-603f-4b25-b182-ce7b0f8291d8 
               
               
                 { 
               
               
                    “eventseriesId”: “c7c157e4-603f-4b25-b182-ce7b0f8291d8”, 
               
               
                    “startTimestamp”: “2017-04-01 13:48:23-05:00”, 
               
               
                    “endTimestamp”: “2017-04-01 13:54:11-05:00”, 
               
               
                    “state”: “High Pressure Alarm” 
               
               
                 } 
               
               
                   
               
            
           
         
       
     
     In response to this request, event service  603  can update the specified properties of the event designated by EventseriesID “c7c157e4-603f-4b25-b182-ce7b0f8291d8.” The updated event may have the start time “2017-04-01 13:48:23-05:00,” the end time “2017-04-01 13:54:11-05:00,” and the state “High Pressure Alarm” as specified in the request. In some embodiments, event service  603  responds to the request by acknowledging that the event has been updated. 
     In some embodiments, event service  603  is configured to read the events of an eventseries. For example, event service  603  can receive a request to identify all of the events associated with an eventseries. The request may be specified as a get request as follows: 
       Get {timeseriesV2}/eventseries/c7c157e4-603f-4b25-b182-ce7b0M91d8/events 
     where “c7c157e4-603f-4b25-b182-ce7b0f8291d8” is the EventseriesID of a specific eventseries. 
     In response to this request, event service  603  can search for all events of the specified eventseries and can return a list of the identified events. An example response which can be provided by event service  603  is as follows: 
                                [          {             “eventid”: “g9c197e4-003f-4u25-b182-se7b0f1945y”,             “eventseriesId”: “c7c157e4-603f-4b25-b182-ce7b0f8291d8”,             “startTimestamp”: “2017-04-01 13:48:23-05:00”,             “endTimestamp”: “2017-04-01 13:54:11-05:00”,             “state”: “High Pressure Alarm”          }       ]                    
where “g9c197e4-003f-4u25-b182-se7b0f81945y” is the EventID of an identified event matching the search parameters. The response may specify the EventseriesID, StartTimestamp, EndTimestamp, and State properties of each identified event.
 
     In some embodiments, event service  603  is configured to search for the events of an eventseries that have a specific state. For example, event service  603  can receive a request to identify all of the events associated with a particular eventseries which have a specific state. The request may be specified as a get request as follows: 
                                        Get {timeseriesV2}/eventseries/c7c157e4-603f-4b25-b182-           ce7b0f8291d8/events?state=Hot                    
where “c7c157e4-603f-4b25-b182-ce7b0f8291d8” is the EventseriesID of a particular eventseries and “state=Hot” specifies that the search should return only events of the eventseries that have the state “Hot.” In response to this request, event service  603  may search for all matching events (i.e., events of the specified eventseries that have the specified state) and may return a list of events that match the search parameters.
 
     In some embodiments, event service  603  is configured to search for the events of an eventseries that have a start time or end time matching a given value. For example, event service  603  can receive a request to identify all of the events of a particular eventseries that have a start time or end time that matches a specified timestamp. The request may be specified as a get request as follows: 
                                Get {timeseriesV2}/eventseries/c7c157e4-603f-4b25-b182-       ce7b0f8291d8/events?startTime=2017-04-01%2010:00 00-05:00&amp;endTime=2017-04-       01%2010:00:00-05:00                    
where “c7c157e4-603f-4b25-b182-ce7b0f8291d8” is the EventseriesID of a particular eventseries and the “startTime” and “endTime” parameters specify the start time and end time of the event. In response to this request, event service  603  may search for all matching events (i.e., (startTimestamp of event &lt;startTime and endTimestamp of event &gt;endTime) and may return a list of events that match the search parameters.
 
     In some embodiments, event service  603  is configured to search for the events of an eventseries that have a time range overlapping (at least partially) with a specified time range. For example, event service  603  can receive a request to identify all of the events of a particular eventseries that have (1) an event start time before a specified start time and an event end time after the specified start time or (2) an event start time before a specified end time and an event end time after the specified end time. The request may be specified as a get request as follows: 
                                Get {timeseriesV2}/eventseries/c7c157e4-603f-4b25-b182-       ce7b0f8291d8/events?startTime=2017-04-01%2010:00:00-05:00&amp;endTime=2017-04-       01%2011:59:00-05:00                    
where “c7c157e4-603f-4b25-b182-ce7b0f8291d8” is the EventseriesID of a particular eventseries and the “startTime” and “endTime” parameters specify the start time and end time of the event. In response to this request, event service  603  may search for all events that match the following criteria:
 
                                [(startTimestamp of event &lt; startTime of query) AND (endTimestamp of event &gt;       startTime of query)] OR [(startTimestamp of event &lt; endTime of query) AND       (endTimestamp of event &gt; endTime of query)]                    
and may return a list of events that match these criteria.
 
     In some embodiments, event service  603  is configured to search for events of an eventseries that have a specific state and a time range that overlaps (at least partially) with a given time range. For example, event service  603  can receive a request to identify all of the events of a particular eventseries that have a particular state and either (1) an event start time before a specified start time and an event end time after the specified start time or (2) an event start time before a specified end time and an event end time after the specified end time. The request may be specified as a get request as follows: 
                                        Get {timeseriesV2}/eventseries/c7c157e4-603f-4b25-b182-           ce7b0f8291d8/events?state=Hot&amp;startTime=2017-04-01%2010:00:00-           05:00&amp;endTime=2017-04-01%2011:59:00-05:00                    
where “c7c157e4-603f-4b25-b182-ce7b0f8291d8” is the EventseriesID of a particular eventseries, the “state” parameter specifies a particular state, and the “startTime” and “endTime” parameters specify the start time and end time of the event. In response to this request, event service  603  may search for all events that match the following criteria:
 
                                State=Hot AND       [(startTimestamp of event &lt; startTime of query) AND (endTimestamp of event &gt;       startTime of query)] OR [(startTimestamp of event &lt; endTime of query) AND       (endTimestamp of event &gt; endTime of query)]                    
and may return a list of events that match these criteria.
 
     Directed Acyclic Graphs 
     Referring again to  FIG. 6 , timeseries processing engine  604  is shown to include a directed acyclic graph (DAG) generator  620 . DAG generator  620  can be configured to generate one or more DAGs for each raw data timeseries. Each DAG may define a workflow or sequence of operations which can be performed by timeseries operators  606  on the raw data timeseries. When new samples of the raw data timeseries are received, workflow manager  622  can retrieve the corresponding DAG and use the DAG to determine how the raw data timeseries should be processed. In some embodiments, the DAGs are declarative views which represent the sequence of operations applied to each raw data timeseries. The DAGs may be designed for timeseries rather than structured query language (SQL). 
     In some embodiments, DAGs apply over windows of time. For example, the timeseries processing operations defined by a DAG may include a data aggregation operation that aggregates a plurality of raw data samples having timestamps within a given time window. The start time and end time of the time window may be defined by the DAG and the timeseries to which the DAG is applied. The DAG may define the duration of the time window over which the data aggregation operation will be performed. For example, the DAG may define the aggregation operation as an hourly aggregation (i.e., to produce an hourly data rollup timeseries), a daily aggregation (i.e., to produce a daily data rollup timeseries), a weekly aggregation (i.e., to produce a weekly data rollup timeseries), or any other aggregation duration. The position of the time window (e.g., a specific day, a specific week, etc.) over which the aggregation is performed may be defined by the timestamps of the data samples of timeseries provided as an input to the DAG. 
     In operation, sample aggregator  608  can use the DAG to identify the duration of the time window (e.g., an hour, a day, a week, etc.) over which the data aggregation operation will be performed. Sample aggregator  608  can use the timestamps of the data samples in the timeseries provided as an input to the DAG to identify the location of the time window (i.e., the start time and the end time). Sample aggregator  608  can set the start time and end time of the time window such that the time window has the identified duration and includes the timestamps of the data samples. In some embodiments, the time windows are fixed, having predefined start times and end times (e.g., the beginning and end of each hour, day, week, etc.). In other embodiments, the time windows may be sliding time windows, having start times and end times that depend on the timestamps of the data samples in the input timeseries. 
     Referring now to  FIG. 10A , an example of a DAG  1000  which can be created by DAG generator  620  is shown, according to an exemplary embodiment. DAG  1000  is shown as a structured tree representing a graph of the dataflow rather than a formal scripting language. Blocks  1002  and  1004  represent the input timeseries which can be specified by timeseries ID (e.g., ID 123, ID 456, etc.). Blocks  1006  and  1008  are functional blocks representing data cleansing operations. Similarly, block  1010  is a functional block representing a weekly rollup aggregation and block  1012  is a functional block representing an addition operation. Blocks  1014  and  1016  represent storage operations indicating where the output of DAG  1000  should be stored (e.g., local storage, hosted storage, etc.). 
     In DAG  1000 , the arrows connecting blocks  1002 - 1016  represent the flow of data and indicate the sequence in which the operations defined by the functional blocks should be performed. For example, the cleansing operation represented by block  1006  will be the first operation performed on the timeseries represented by block  1002 . The output of the cleansing operation in block  1006  will then be provided as an input to both the aggregation operation represented by block  1010  and the addition operation represented by block  1012 . Similarly, the cleansing operation represented by block  1008  will be the first operation performed on the timeseries represented by block  1004 . The output of the cleansing operation in block  1008  will then be provided as an input to the addition operation represented by block  1012 . 
     In some embodiments, DAG  1000  can reference other DAGs as inputs. Timeseries processing engine  604  can stitch the DAGs together into larger groups. DAG  1000  can support both scalar operators (e.g., run this function on this sample at this timestamp) and aggregate window operators (e.g., apply this function over all the values in the timeseries from this time window). The time windows can be arbitrary and are not limited to fixed aggregation windows. Logical operators can be used to express rules and implement fault detection algorithms. In some embodiments, DAG  1000  supports user-defined functions and user-defined aggregates. 
     In some embodiments, DAG  1000  is created based on user input. A user can drag-and-drop various input blocks  1002 - 1004 , functional blocks  1006 - 1012 , and output blocks  1014 - 1016  into DAG  1000  and connect them with arrows to define a sequence of operations. The user can edit the operations to define various parameters of the operations. For example, the user can define parameters such as upper and lower bounds for the data cleansing operations in blocks  1006 - 1008  and an aggregation interval for the aggregation operation in block  1010 . DAG  1000  can be created and edited in a graphical drag-and-drop flow editor without requiring the user to write or edit any formal code. In some embodiments, DAG generator  620  is configured to automatically generate the formal code used by timeseries operators  606  based on DAG  1000 . 
     Referring now to  FIG. 10B , an example of code  1050  which can be generated by DAG generator  620  is shown, according to an exemplary embodiment. Code  1050  is shown as a collection of JSON objects  1052 - 1056  that represent the various operations defined by DAG  1000 . Each JSON object corresponds to one of the functional blocks in DAG  1000  and specifies the inputs/sources, the computation, and the outputs of each block. For example, object  1052  corresponds to the cleansing operation represented by block  1006  and defines the input timeseries (i.e., “123_Raw”), the particular cleansing operation to be performed (i.e., “BoundsLimitingCleanseOP”), the parameters of the cleansing operation (i.e., “upperbound” and “lowerbound”) and the outputs of the cleansing operation (i.e., “123_Cleanse” and “BLCleanseFlag”). 
     Similarly, object  1054  corresponds to the aggregation operation represented by block  1010  and defines the input timeseries (i.e., “123_Cleanse”), the aggregation operation to be performed (i.e., “AggregateOP”), the parameter of the aggregation operation (i.e., “interval”: “week”) and the output of the aggregation operation (i.e., “123_WeeklyRollup”). Object  1056  corresponds to the addition operation represented by block  1012  and defines the input timeseries (i.e., “123_Cleanse” and “456_Cleanse”), the addition operation to be performed (i.e., “AddOP”), and the output of the addition operation (i.e., “123+456”). Although not specifically shown in  FIG. 10B , code  1050  may include an object for each functional block in DAG  1000 . 
     Advantageously, the declarative views defined by the DAGs provide a comprehensive view of the operations applied to various input timeseries. This provides flexibility to run the workflow defined by a DAG at query time (e.g., when a request for derived timeseries data is received) or prior to query time (e.g., when new raw data samples are received, in response to a defined event or trigger, etc.). This flexibility allows timeseries processing engine  604  to perform some or all of their operations ahead of time and/or in response to a request for specific derived data timeseries. 
     Referring again to  FIG. 6 , timeseries processing engine  604  is shown to include a DAG optimizer  618 . DAG optimizer  618  can be configured to combine multiple DAGs or multiple steps of a DAG to improve the efficiency of the operations performed by timeseries operators  606 . For example, suppose that a DAG has one functional block which adds “Timeseries A” and “Timeseries B” to create “Timeseries C” (i.e., A+B=C) and another functional block which adds “Timeseries C” and “Timeseries D” to create “Timeseries E” (i.e., C+D=E). DAG optimizer  618  can combine these two functional blocks into a single functional block which computes “Timeseries E” directly from “Timeseries A,” “Timeseries B,” and “Timeseries D” (i.e., E=A+B+D). Alternatively, both “Timeseries C” and “Timeseries E” can be computed in the same functional block to reduce the number of independent operations required to process the DAG. 
     In some embodiments, DAG optimizer  618  combines DAGs or steps of a DAG in response to a determination that multiple DAGs or steps of a DAG will use similar or shared inputs (e.g., one or more of the same input timeseries). This allows the inputs to be retrieved and loaded once rather than performing two separate operations that both load the same inputs. In some embodiments, DAG optimizer  618  schedules timeseries operators  606  to nodes where data is resident in memory in order to further reduce the amount of data required to be loaded from timeseries databases  628  and  636 . 
     Entity Graph 
     Referring now to  FIG. 11A , an entity graph  1100  is shown, according to some embodiments. In some embodiments, entity graph  1100  is generated or used by data collector  512 , as described with reference to  FIG. 5 . Entity graph  1100  describes how a building is organized and how the different systems and spaces within the building relate to each other. For example, entity graph  1100  is shown to include an organization  1102 , a space  1104 , a system  1106 , a point  1108 , and a timeseries  1109 . The arrows interconnecting organization  1102 , space  1104 , system  1106 , point  1108 , and timeseries  1109  identify the relationships between such entities. In some embodiments, the relationships are stored as attributes of the entity described by the attribute. 
     Organization  1102  is shown to include a contains descendants attribute  1110 , a parent ancestors attribute  1112 , a contains attribute  1114 , a located in attribute  1116 , an occupied by ancestors attribute  1118 , and an occupies by attribute  1122 . The contains descendants attribute  1110  identifies any descendant entities contained within organization  1102 . The parent ancestors attribute  1112  identifies any parent entities to organization  1102 . The contains attribute  1114  identifies any other organizations contained within organization  1102 . The asterisk alongside the contains attribute  1114  indicates that organization  1102  can contain any number of other organizations. The located in attribute  1116  identifies another organization within which organization  1102  is located. The number 1 alongside the located in attribute  1116  indicates that organization  1102  can be located in exactly one other organization. The occupies attribute  1122  identifies any spaces occupied by organization  1102 . The asterisk alongside the occupies attribute  1122  indicates that organization  1102  can occupy any number of spaces. 
     Space  1104  is shown to include an occupied by attribute  1120 , an occupied by ancestors attribute  1118 , a contains space descendants attribute  1124 , a located in ancestors attribute  1126 , a contains spaces attribute  1128 , a located in attribute  1130 , a served by systems attribute  1138 , and a served by system descendants attribute  1134 . The occupied by attribute  1120  identifies an organization occupied by space  1104 . The number 1 alongside the occupied by attribute  1120  indicates that space  1104  can be occupied by exactly one organization. The occupied by ancestors attribute  1118  identifies one or more ancestors to organization  1102  that are occupied by space  1104 . The asterisk alongside the occupied by ancestors attribute  1118  indicates that space  1104  can be occupied by any number of ancestors. 
     The contains space descendants attribute  1124  identifies any descendants to space  1104  that are contained within space  1104 . The located in ancestors attribute  1126  identifies any ancestors to space  1104  within which space  1104  is located. The contains spaces attribute  1128  identifies any other spaces contained within space  1104 . The asterisk alongside the contains spaces attribute  1128  indicates that space  1104  can contain any number of other spaces. The located in attribute  1130  identifies another space within which space  1104  is located. The number 1 alongside the located in attribute  1130  indicates that space  1104  can be located in exactly one other space. The served by systems attribute  1138  identifies any systems that serve space  1104 . The asterisk alongside the served by systems attribute  1138  indicates that space  1104  can be served by any number of systems. The served by system descendants attribute  1134  identifies any descendent systems that serve space  1104 . The asterisk alongside the served by descendant systems attribute  1134  indicates that space  1104  can be served by any number of descendant systems. 
     System  1106  is shown to include a serves spaces attribute  1136 , a serves space ancestors attribute  1132 , a subsystem descendants attribute  1140 , a part of ancestors attribute  1142 , a subsystems attribute  1144 , a part of attribute  1146 , and a points attribute  1150 . The serves spaces attribute  1136  identifies any spaces that are served by system  1106 . The asterisk alongside the serves spaces attribute  1136  indicates that system  1106  can serve any number of spaces. The serves space ancestors attribute  1132  identifies any ancestors to space  1104  that are served by system  1106 . The asterisk alongside the serves ancestor spaces attribute  1132  indicates that system  1106  can serve any number of ancestor spaces. 
     The subsystem descendants attribute  1140  identifies any subsystem descendants of other systems contained within system  1106 . The part of ancestors attribute  1142  identifies any ancestors to system  1106  that system  1106  is part of. The subsystems attribute  1144  identifies any subsystems contained within system  1106 . The asterisk alongside the subsystems attribute  1144  indicates that system  1106  can contain any number of subsystems. The part of attribute  1146  identifies any other systems that system  1106  is part of. The number 1 alongside the part of attribute  1146  indicates that system  1106  can be part of exactly one other system. The points attribute  1150  identifies any data points that are associated with system  1106 . The asterisk alongside the points attribute  1150  indicates that any number of data points can be associated with system  1106 . 
     Point  1108  is shown to include a used by system attribute  1148 . The asterisk alongside the used by system attribute  1148  indicates that point  1108  can be used by any number of systems. Point  1108  is also shown to include a used by timeseries attribute  1154 . The asterisk alongside the used by timeseries attribute  1154  indicates that point  1108  can be used by any number of timeseries (e.g., raw data timeseries virtual point timeseries, data rollup timeseries, etc.). For example, multiple virtual point timeseries can be based on the same actual data point  1108 . In some embodiments, the used by timeseries attribute  1154  is treated as a list of timeseries that subscribe to changes in value of data point  1108 . When the value of point  1108  changes, the timeseries listed in the used by timeseries attribute  1154  can be identified and automatically updated to reflect the changed value of point  1108 . 
     Timeseries  1109  is shown to include a uses point attribute  1152 . The asterisk alongside the uses point attribute  1152  indicates that timeseries  1109  can use any number of actual data points. For example, a virtual point timeseries can be based on multiple actual data points. In some embodiments, the uses point attribute  1152  is treated as a list of points to monitor for changes in value. When any of the points identified by the uses point attribute  1152  are updated, timeseries  1109  can be automatically updated to reflect the changed value of the points used by timeseries  1109 . 
     Timeseries  1109  is also shown to include a used by timeseries attribute  1156  and a uses timeseries attribute  1158 . The asterisks alongside the used by timeseries attribute  1156  and the uses timeseries attribute  1158  indicate that timeseries  1109  can be used by any number of other timeseries and can use any number of other timeseries. For example, both a data rollup timeseries and a virtual point timeseries can be based on the same raw data timeseries. As another example, a single virtual point timeseries can be based on multiple other timeseries (e.g., multiple raw data timeseries). In some embodiments, the used by timeseries attribute  1156  is treated as a list of timeseries that subscribe to updates in timeseries  1109 . When timeseries  1109  is updated, the timeseries listed in the used by timeseries attribute  1156  can be identified and automatically updated to reflect the change to timeseries  1109 . Similarly, the uses timeseries attribute  1158  can be treated as a list of timeseries to monitor for updates. When any of the timeseries identified by the uses timeseries attribute  1158  are updated, timeseries  1109  can be automatically updated to reflect the updates to the other timeseries upon which timeseries  1109  is based. 
     Referring now to  FIG. 11B , an example of an entity graph  1160  for a particular building management system is shown, according to some embodiments. Entity graph  1160  is shown to include an organization  1161  (“ACME Corp”). Organization  1161  be a collection of people, a legal entity, a business, an agency, or other type of organization. Organization  1161  occupies space  1163  (“Milwaukee Campus”), as indicated by the occupies attribute  1164 . Space  1163  is occupied by organization  1161 , as indicated by the occupied by attribute  1162 . 
     In some embodiments, space  1163  is a top level space in a hierarchy of spaces. For example, space  1163  can represent an entire campus (i.e., a collection of buildings). Space  1163  can contain various subspaces (e.g., individual buildings) such as space  1165  (“Building  1 ”) and space  1173  (“Building  2 ”), as indicated by the contains attributes  1168  and  1180 . Spaces  1165  and  1180  are located in space  1163 , as indicated by the located in attribute  1166 . Each of spaces  1165  and  1173  can contain lower level subspaces such as individual floors, zones, or rooms within each building. However, such subspaces are omitted from entity graph  1160  for simplicity. 
     Space  1165  is served by system  1167  (“ElecMainMeter1”) as indicated by the served by attribute  1172 . System  1167  can be any system that serves space  1165  (e.g., a HVAC system, a lighting system, an electrical system, a security system, etc.). The serves attribute  1170  indicates that system  1167  serves space  1165 . In entity graph  1160 , system  1167  is shown as an electrical system having a subsystem  1169  (“LightingSubMeter1”) and a subsystem  1171  (“PlugLoadSubMeter2”) as indicated by the subsystem attributes  1176  and  1178 . Subsystems  1169  and  1171  are part of system  1167 , as indicated by the part of attribute  1174 . 
     Space  1173  is served by system  1175  (“ElecMainMeter2”) as indicated by the served by attribute  1184 . System  1175  can be any system that serves space  1173  (e.g., a HVAC system, a lighting system, an electrical system, a security system, etc.). The serves attribute  1182  indicates that system  1175  serves space  1173 . In entity graph  1160 , system  1175  is shown as an electrical system having a subsystem  1177  (“LightingSubMeter3”) as indicated by the subsystem attribute  1188 . Subsystem  1177  is part of system  1175 , as indicated by the part of attribute  1186 . 
     In addition to the attributes shown in  FIG. 11B , entity graph  1160  can include “ancestors” and “descendants” attributes on each entity in the hierarchy. The ancestors attribute can identify (e.g., in a flat list) all of the entities that are ancestors to a given entity. For example, the ancestors attribute for space  1165  may identify both space  1163  and organization  1161  as ancestors. Similarly, the descendants attribute can identify all (e.g., in a flat list) of the entities that are descendants of a given entity. For example, the descendants attribute for space  1165  may identify system  1167 , subsystem  1169 , and subsystem  1171  as descendants. This provides each entity with a complete listing of its ancestors and descendants, regardless of how many levels are included in the hierarchical tree. This is a form of transitive closure. 
     In some embodiments, the transitive closure provided by the descendants and ancestors attributes allows entity graph  1160  to facilitate simple queries without having to search multiple levels of the hierarchical tree. For example, the following query can be used to find all meters under the Milwaukee Campus space  1163 : 
                                        /Systems?$filter=(systemType eq Jci.Be.Data.SystemType′Meter′)           and ancestorSpaces/any(a:a/name eq ′Milwaukee Campus′)                    
and can be answered using only the descendants attribute of the Milwaukee Campus space  1163 . For example, the descendants attribute of space  1163  can identify all meters that are hierarchically below space  1163 . The descendants attribute can be organized as a flat list and stored as an attribute of space  1163 . This allows the query to be served by searching only the descendants attribute of space  1163  without requiring other levels or entities of the hierarchy to be searched.
 
     Referring now to  FIG. 12 , an object relationship diagram  1200  is shown, according to some embodiments. Relationship diagram  1200  is shown to include an entity template  1202 , a point  1204 , a timeseries  1206 , and a sample  1208 . In some embodiments, entity template  1202 , point  1204 , timeseries  1206 , and sample  1208  are stored as data objects within memory  510 , local storage  514 , and/or hosted storage  516 . Relationship diagram  1200  illustrates the relationships between entity template  1202 , point  1204 , and timeseries  1206 . 
     Entity template  1202  can include various attributes such as an ID attribute, a name attribute, a properties attribute, and a relationships attribute. The ID attribute can be provided as a text string and identifies a unique ID for entity template  1202 . The name attribute can also be provided as a text string and identifies the name of entity template  1202 . The properties attribute can be provided as a vector and identifies one or more properties of entity template  1202 . The relationships attribute can also be provided as a vector and identifies one or more relationships of entity template  1202 . 
     Point  1204  can include various attributes such as an ID attribute, an entity template ID attribute, a timeseries attribute, and a units ID attribute. The ID attribute can be provided as a text string and identifies a unique ID for point  1204 . The entity template ID attribute can also be provided as a text string and identifies the entity template  1202  associated with point  1204  (e.g., by listing the ID attribute of entity template  1202 ). Any number of points  1204  can be associated with entity template  1202 . However, in some embodiments, each point  1204  is associated with a single entity template  1202 . The timeseries attribute can be provided as a text string and identifies any timeseries associated with point  1204  (e.g., by listing the ID string of any timeseries  1206  associated with point  1204 ). The units ID attribute can also be provided as a text string and identifies the units of the variable quantified by point  1204 . 
     Timeseries  1206  can include various attributes such as an ID attribute, a samples attribute, a transformation type attribute, and a units ID attribute. The ID attribute can be provided as a text string and identifies a unique ID for timeseries  1206 . The unique ID of timeseries  1206  can be listed in the timeseries attribute of point  1204  to associate timeseries  1206  with point  1204 . Any number of timeseries  1206  can be associated with point  1204 . Each timeseries  1206  is associated with a single point  1204 . The samples attribute can be provided as a vector and identifies one or more samples associated with timeseries  1206 . The transformation type attribute identifies the type of transformation used to generate timeseries  1206  (e.g., average hourly, average daily, average monthly, etc.). The units ID attribute can also be provided as a text string and identifies the units of the variable quantified by timeseries  1206 . 
     Sample  1208  can include a timestamp attribute and a value attribute. The timestamp attribute can be provided in local time and can include an offset relative to universal time. The value attribute can include a data value of sample  1208 . In some instances, the value attribute is a numerical value (e.g., for measured variables). In other instances, the value attribute can be a text string such as “Fault” if sample  1208  is part of a fault detection timeseries. 
     Timeseries Processing Workflow 
     Referring now to  FIG. 13A , a block diagram illustrating a timeseries processing workflow  1300  is shown, according to an exemplary embodiment. Workflow  1300  may be performed by workflow manager  622  in combination with other components of timeseries service  528 . Workflow  1300  is shown to include performing a read of the timeseries data (step  1302 ). Step  1302  may include reading raw data samples and/or the derived data samples provided by timeseries storage interface  616 . The timeseries data may be stored in local storage  514  or hosted storage  516 . In some embodiments, local storage  514  includes on-site data storage (e.g., Redis, PostgreSQL, etc.). Hosted storage  516  may include cloud data storage (e.g., Azure Redis, DocDB, HBase, etc.). 
     Timeseries storage interface  616  can be configured to read and write a timeseries collection, a samples collection, and a post sample request (PSR) collection. Each of these collections can be stored in local storage  514  and/or hosted storage  516 . The timeseries collection may contain all the timeseries registered in workflow manager  622 . The timeseries collection may also contain the DAG for each timeseries. The timeseries collection can be used by workflow manager  622  to accept only PSRs related to valid timeseries registered in workflow manager  622 . The timeseries collection can also be used in steps  1314 - 1316  to lookup the DAG for a specific timeseries ID. 
     In some embodiments, the entire timeseries collection is loaded into local memory. The timeseries collection can be a regular collection or a partitioned collection (e.g., one partition for approximately every 100 timeseries). In some embodiments, the timeseries collection contains about 200,000 to 250,000 timeseries. The ID for each document in the timeseries collection may be the timeseries ID. The DAG for each timeseries may contain a set of operations and/or transformations that need to be performed to generate the derived timeseries data based on the timeseries. On registration of a new timeseries, the DAG for the timeseries can be selected from DAG templates. The DAG template may include a set of standard operations applicable to the timeseries. On definition of a new metric for a timeseries, the new metric and the list of operations to generate that metric can be added to the DAG. 
     The samples collection may contain all of the timeseries samples (e.g., raw samples, derived timeseries samples). The samples collection can be used for all GET requests for a specific timeseries ID. A portion of the samples collection can be stored in local memory (e.g., past 48 hours) whereas the remainder of the samples collection can be stored in local storage  514  or hosted storage  516 . The samples collection may act as a partitioned collection instead of a regular collection to improve efficiency and performance. In some embodiments, the samples collection is stored in a JSON format and partitioned on timeseries ID. The ID field may be unique for each partition and may have the form “Metric: Timestamp.” 
     The PSR collection may contain all of the PSRs and can be used to provide status updates to the user for a PSR related to a specific timeseries ID. A portion of the PSR collection can be stored in local memory (e.g., past 48 hours) whereas the remainder of the PSR collection can be stored in local storage  514  or hosted storage  516 . The PSR collection can be partitioned on timeseries ID. In some embodiments, the ID for each document in the PSR collection has the form “TimeseriesID: Timestamp.” 
     Still referring to  FIG. 13A , workflow  1300  is shown to include accepting a PSR (step  1304 ). Step  1304  may be performed by executing a PSR process. In some embodiments, the PSR process receives a PSR and determines whether the PSR contains more than one timeseries ID. In response to a determination that the PSR contains more than one timeseries ID, the PSR process may break the PSR into multiple PSRs, each of which is limited to a single timeseries ID. The PSRs can be provided to PSR event hub  1306 . PSR event hub  1306  can be configured to store PSR events. Each PSR event may include a PSR for one timeseries ID. In some embodiments, each PSR event is stored in the form “TimeseriesID: Timestamp.” 
     Workflow  1300  is shown to include deduplicating raw samples (step  1308 ). Step  1308  may be performed by executing a deduplication process. In some embodiments, the deduplication process includes accepting PSR events from PSR event hub  1306  and splitting each PSR into a list of samples. Step  1308  may include tagging each sample as a new sample, an updated sample, or a duplicate sample. New samples and updated samples can be sent to raw samples event hub  1310 , whereas duplicate samples may be discarded. In some embodiments, step  1308  is deployed on Azure using Azure Worker Roles. Step  1308  can include checking for duplicate samples in local storage  514  and hosted storage  516  as well as the samples that are currently in raw samples event hub  1310 . 
     In some embodiments, the deduplication process in step  1308  removes all duplicate data samples such that only a single unique copy of each data sample remains. Removing all duplicate samples may ensure that aggregate operations produce accurate aggregate values. In other embodiments, the deduplication process in step  1308  is configured to remove most, but not all, duplicate samples. For example, the deduplication process can be implemented using a Bloom filter, which allows for the possibility of false positives but not false negatives. In step  1308 , a false positive can be defined as a non-duplicate new or updated sample. Accordingly, some duplicates may be flagged as non-duplicate, which introduces the possibility that some duplicate samples may not be properly identified and removed. The deduplicated raw samples can be sent to raw samples event hub  1310 . 
     Workflow  1300  is shown to include storing the raw samples (step  1312 ). Step  1312  can include accepting the raw samples from raw samples event hub  1310  and pushing the raw samples to persistent storage. In some embodiments, step  1312  is deployed on Azure using Azure Worker Roles. The worker role may generate requests at a rate based on X % of the capacity of the storage. For example, if the capacity of the storage is 10,000 RU and X % is 20% (e.g., 20% of the storage throughput is reserved for raw sample writes), and each write takes 5 RU, step  1312  may generate a total of 400 writes per second 
     
       
         
           
             
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     Workflow  1300  is shown to include generating an event trigger DAG (step  1314 ). Step  1314  can be performed by executing an event trigger DAG process. Step  1314  may include accepting events (samples) from raw samples event hub  1310 . For each sample event, step  1314  may include identifying the timeseries ID of the sample and accessing the timeseries collection to obtain the DAG for the corresponding timeseries. Step  1314  may include identifying each derived data timeseries generated by the DAG and each operation included in the DAG. In some embodiments, step  1314  tags each operation to indicate whether the operation should be sent to the C# engine  1332  or the Python engine  1334  for execution. Step  1314  may include identifying and fetching any additional data (e.g., samples, timeseries, parameters, etc.) which may be necessary to perform the operations defined by the DAG. Step  1314  may generate an enriched DAG which includes the original DAG along with all the data necessary to perform the operations defined by the DAG. The enriched DAG can be sent to the DAG event hub  1318 . 
     In some embodiments, workflow  1300  includes generating a clock trigger DAG (step  1316 ). Step  1316  can be performed by executing a clock trigger DAG process. Step  1316  may be similar to step  1314 . However, step  1316  may be performed in response to a clock trigger rather than in response to receiving a raw sample event. The clock trigger can periodically trigger step  1316  to perform batch queries (e.g., every hour). Step  1316  may include identifying a timeseries ID specified in the clock trigger and accessing the timeseries collection to obtain the DAG for the corresponding timeseries. Step  1316  may include identifying each derived data timeseries generated by the DAG and each operation included in the DAG. In some embodiments, step  1316  tags each operation to indicate whether the operation should be sent to the C# engine  1332  or the Python engine  1334  for execution. Step  1316  may include identifying and fetching any additional data (e.g., samples, timeseries, parameters, etc.) which may be necessary to perform the operations defined by the DAG. Step  1316  may generate an enriched DAG which includes the original DAG along with all the data necessary to perform the operations defined by the DAG. The enriched DAG can be sent to the DAG event hub  1318 . 
     DAG event hub  1318  can be configured to store enriched DAG events. Each enriched DAG event can include an enriched DAG. The enriched DAG may include a DAG for a particular timeseries along with all the data necessary to perform the operations defined by the DAG. DAG event hub  1318  can provide the enriched DAG events to step  1320 . 
     Still referring to  FIG. 13A , workflow  1300  is shown to include running the DAG (step  1320 ). Step  1320  can include accepting enriched DAG events from DAG event hub  1318  and running through the sequence of operations defined by the DAG. Workflow manager  622  can submit each operation in series to execution engines  1330  and wait for results before submitting the next operation. Execution engines  1330  may include a C# engine  1332 , a Python engine  1334 , or any other engine configured to perform the operations defined by the DAG. In some embodiments, execution engines  1330  include timeseries operators  606 . When a given operation is complete, execution engines  1330  can provide the results of the operation to workflow manager  622 . Workflow manager  622  can use the results of one or more operations as inputs for the next operation, along with any other inputs that are required to perform the operation. In some embodiments, the results of the operations are the derived timeseries samples. The derived timeseries samples can be provided to derived timeseries event hub  1322 . 
     Derived timeseries event hub  1322  can be configured to store derived timeseries events. Each derived timeseries event may include a sample of an derived timeseries. The derived timeseries may include the results of the operations performed by execution engines  1330 . Derived timeseries event hub  1322  can provide the derived timeseries samples to step  1324 . 
     Workflow  1300  is shown to include storing the derived timeseries samples (step  1324 ). Step  1324  can include accepting derived timeseries samples from derived timeseries event hub  1322  and storing the derived timeseries samples in persistent storage (e.g., local storage  514 , hosted storage  516 ). In some embodiments, step  1324  is deployed on Azure using Azure Worker Roles. The worker role may generate requests at a rate based on Y % of the capacity of the storage. For example, if the capacity of the storage is 10,000 RU and Y % is 50% (e.g., 50% of the storage throughput is reserved for raw sample writes), and each write takes 5 RU, step  1324  may generate a total of 1,000 writes per second 
     
       
         
           
             
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     Referring now to  FIG. 13B , a flowchart of a process  1350  for obtaining and processing timeseries data is shown, according to an exemplary embodiment. Process  1350  can be performed by workflow manager  622  in combination with other components of timeseries service  528 . Process  1350  is shown to include obtaining samples of a timeseries from timeseries storage (step  1352 ). Step  1352  may include obtaining raw data samples and/or derived data samples via timeseries storage interface  616 . The samples of the timeseries may be obtained from local storage  514 , hosted storage  516 , or received in real-time from a sensor or other device that generates the samples. Step  1352  can include loading the entire timeseries or a subset of the samples of the timeseries into local memory. For example, some of the samples of the timeseries may be stored in local memory (e.g., past 48 hours) whereas the remainder of the samples of the timeseries can be stored in local storage  514  or hosted storage  516 . 
     Process  1350  is shown to include handling a post-sample request (PSR) associated with the timeseries (step  1354 ). The PSR may be obtained from a PSR collection via timeseries storage interface  616 . The PSR can be used to provide status updates to the user for a specific timeseries ID. In some embodiments, step  1354  includes receiving a PSR and determining whether the PSR contains more than one timeseries ID. In response to a determination that the PSR contains more than one timeseries ID, step  1354  may include breaking the PSR into multiple PSRs, each of which is limited to a single timeseries ID. The PSRs can be provided to PSR event hub  1306  and stored as PSR events. Each PSR event may include a PSR for one timeseries ID. In some embodiments, each PSR event is stored in the form “TimeseriesID: Timestamp.” 
     Process  1350  is shown to include deduplicating samples of the timeseries (step  1356 ). Step  1356  may be performed by executing a deduplication process. In some embodiments, the deduplication process includes accepting PSR events from PSR event hub  1306  and splitting each PSR into a list of samples. Step  1356  may include tagging each sample as a new sample, an updated sample, or a duplicate sample. New samples and updated samples can be sent to raw samples event hub  1310 , whereas duplicate samples may be discarded. In some embodiments, step  1356  is deployed on Azure using Azure Worker Roles. Step  1356  can include checking for duplicate samples in local storage  514  and hosted storage  516  as well as the samples that are currently in raw samples event hub  1310 . 
     In some embodiments, the deduplication process in step  1356  removes all duplicate data samples such that only a single unique copy of each data sample remains. Removing all duplicate samples may ensure that aggregate operations produce accurate aggregate values. In other embodiments, the deduplication process in step  1356  is configured to remove most, but not all, duplicate samples. For example, the deduplication process can be implemented using a Bloom filter, which allows for the possibility of false positives but not false negatives. In step  1356 , a false positive can be defined as a non-duplicate new or updated sample. Accordingly, some duplicates may be flagged as non-duplicate, which introduces the possibility that some duplicate samples may not be properly identified and removed. The deduplicated samples can be sent to raw samples event hub  1310 . 
     Still referring to  FIG. 13B , process  1350  is shown to include identifying one or more stored DAGs that use the timeseries as an input (step  1358 ). Step  1358  can include obtaining the stored DAGs via timeseries via timeseries storage interface  616  and identifying the required timeseries inputs of each DAG. For each DAG that uses the timeseries as an input, process  1350  can identify the timeseries processing operations defined by the DAG (step  1360 ). The timeseries processing operations can include data cleansing operations, data aggregation operations, timeseries adding operations, virtual point calculation operations, or any other type of operation that can be applied to one or more input timeseries. 
     Process  1350  is shown to include identifying and obtaining samples of any timeseries required to perform the timeseries processing operations (step  1362 ). The timeseries can be identified by inspecting the inputs required by each of the timeseries processing operations identified in step  1360 . For example, DAG  1000  in  FIG. 10A  is shown to include both “Timeseries ID: 123” and “Timeseries ID: 456” as required inputs. Assuming that samples of the timeseries ID 123 are obtained in step  1352 , DAG  1000  can be identified in step  1358  as a DAG that uses the timeseries ID 123 as an input. The timeseries identified in step  1362  can include timeseries ID 123, timeseries ID 456, or any other timeseries used as an input to DAG  1000 . Step  1362  may include identifying and fetching any additional data (e.g., samples, timeseries, parameters, etc.) which may be necessary to perform the operations defined by the DAG. 
     In some embodiments, the samples obtained in step  1362  are based on the timeseries processing operations defined by the DAG, as well as the timestamps of the original samples obtained in step  1352 . For example, the DAG may include a data aggregation operation that aggregates a plurality of data samples having timestamps within a given time window. The start time and end time of the time window may be defined by the DAG and the timeseries to which the DAG is applied. The DAG may define the duration of the time window over which the data aggregation operation will be performed. For example, the DAG may define the aggregation operation as an hourly aggregation (i.e., to produce an hourly data rollup timeseries), a daily aggregation (i.e., to produce a daily data rollup timeseries), a weekly aggregation (i.e., to produce a weekly data rollup timeseries), or any other aggregation duration. The position of the time window (e.g., a specific day, a specific week, etc.) over which the aggregation is performed may be defined by the timestamps of the samples obtained in step  1352 . 
     Step  1362  can include using the DAG to identify the duration of the time window (e.g., an hour, a day, a week, etc.) over which the data aggregation operation will be performed. Step  1362  can include using the timestamps of the data samples obtained in step  1352  identify the location of the time window (i.e., the start time and the end time). Step  1362  can include setting the start time and end time of the time window such that the time window has the identified duration and includes the timestamps of the data samples obtained in step  1352 . In some embodiments, the time windows are fixed, having predefined start times and end times (e.g., the beginning and end of each hour, day, week, etc.). In other embodiments, the time windows may be sliding time windows, having start times and end times that depend on the timestamps of the data samples in the input timeseries. Once the appropriate time window has been set and the other input timeseries are identified, step  1362  can obtain samples of any input timeseries to the DAG that have timestamps within the appropriate time window. The input timeseries can include the original timeseries identified in step  1352  and any other timeseries used as input to the DAG. 
     Process  1350  is shown to include generating an enriched DAG including the original DAG and all timeseries samples required to perform the timeseries processing operations (step  1364 ). The original DAG may be the DAG identified in step  1358 . The timeseries samples required to perform the timeseries processing operations may include any of the timeseries samples obtained in step  1362 . In some embodiments, step  1364  includes identifying each derived data timeseries generated by the DAG and each operation included in the DAG. In some embodiments, step  1364  tags each operation to indicate a particular execution engine (e.g., C# engine  1332 , Python engine  1334 , etc.) to which the processing operation should be sent for execution. 
     Process  1350  is shown to include executing the enriched DAG to generate one or more derived timeseries (step  1366 ). Step  1366  can include submitting each timeseries processing operation in series to execution engines  1330  and waiting for results before submitting the next operation. When a given operation is complete, execution engines  1330  can provide the results of the operation to workflow manager  622 . Process  1350  can use the results of one or more operations as inputs for the next operation, along with any other inputs that are required to perform the operation. In some embodiments, the results of the operations are the derived timeseries samples. 
     Process  1350  is shown to include storing the derived timeseries in the timeseries storage (step  1368 ). The derived timeseries may include the results of the operations performed in step  1366 . Step  1368  can include accepting derived timeseries samples from derived timeseries event hub  1322  and storing the derived timeseries samples in persistent storage (e.g., local storage  514 , hosted storage  516 ). In some embodiments, step  1368  is deployed on Azure using Azure Worker Roles. The worker role may generate requests at a rate based on Y % of the capacity of the storage. For example, if the capacity of the storage is 10,000 RU and Y % is 50% (e.g., 50% of the storage throughput is reserved for raw sample writes), and each write takes 5 RU, step  1368  may generate a total of 1,000 writes per second 
     
       
         
           
             
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     Unified Management and Processing of Data in a Building Management Internet-of-Things (IoT) Environment 
     Data produced and generated by the devices within a BMS can be provided in multiple formats. As technology has changed over time, much of the data produced and generated within the BMS system may be thought of as being essentially multi-media by nature, consisting primarily of telemetry data, meta-data, acoustic signals (e.g. ultrasound), images, video and audio data, as well as text and mathematical notations. In some examples, textual, audio, or video based annotations may be incorporated to allow for specific BMS data to be tagged to provide additional information related to the BMS data. In an IoT based system, as described below, analysis, classification and indexing of IoT data can depend significantly on the ability of the system to recognize the relevant information in multiple data streams, and fuse the recognized data. Fusing the recognized data may transform the collective semantics of the individual data received from multiple devices into semantics consistent with the perception of the real world. However, fusion of the recognized information is difficult between different media and data types. Accordingly, a multi-modal data management system is described below. The multi-modal data management system can provide flexible data processing approaches to maximize information sharing between devices, and to allow for better actionable decision using the fused information. In one specific example, the multi-modal data management system can be configured to apply to unifying event/time series data, such as those described above. 
       FIG. 14  is a block diagram illustrating a silo configured IoT environment  1400 , according to some embodiments. The IoT environment may include a plurality of devices  1402 ,  1404 ,  1406 , a cloud-based service  1408 , and a remote device  1410 . While only three devices  1402 ,  1404 ,  1406  are shown in  FIG. 14 , it is contemplated that the silo configured IoT environment  1400  may include more than three devices or fewer than three devices, as needed. The devices  1402 ,  1404 ,  1406  may be any type of BMS device, such as those described above. For example, the devices  1402 ,  1404 ,  1406  can be sensors, controllers, actuators, sub-systems, thermostats, or any other component within the BMS system capable of communicating to the cloud-based service  1408 . In one embodiments, the devices  1402 ,  1404 ,  1406  may be connected directly to the cloud-based service  1408  via an internet-based connection. For example, the devices  1402 ,  1404 ,  1406  may be connected to the cloud-based service  1408  via a wireless connection such as Wi-Fi. In some embodiments, the devices  1402 ,  1404 ,  1406  are connected to the Internet via one or more gateways, routers, modems, or other internet connected devices, which provide communication to and from the internet. In some examples, the devices  1402 ,  1404 ,  1406  may be configured to communicate directly to the internet. The devices  1402 ,  1404 ,  1406  may include wireless transmitters, such as cellular transmitters (3G, 4G, LTE, CDMA, etc.), that allow the devices  1402 ,  1404 ,  1406  to connect to the internet directly via one or more service providers. 
     As shown in  FIG. 14 , the devices communicate directly to the cloud-based service  1408 . The cloud-based service  1408  may be one or more services provided by a remote server (e.g. the cloud). In one embodiment, the cloud-based service can be a unified management and processing service, as will be described in more detail below. In other embodiments, the cloud-based service  1408  may be a timeseries service, as described above. The remote device  1410  may be one or more devices configured to access the cloud-based service  1408 . In one of the embodiments, the remote device  1410  is a remote computer, such as a Personal Computer (PC). In other embodiments, the remote device  1410  is a mobile device such as a smartphone (Apple iPhone, Android Phone, Windows Phone, etc.), a tablet computer (Apple iPad, Microsoft Surface, Android tablet, etc.). In still further embodiments, the remote device  1410  may be a dedicated device, such as a commissioning tool. In one embodiment, the remote device  1410  is configured to communicate with the one or more cloud based services  1408 . The remote device  1410  may be configured to allow a user to access the cloud-based services  1408 . In some embodiments, a user may be able to request certain actions be performed from the cloud-based service  1408  via the remote device. For example, the remote device  1410  may be used to request certain reports and/or other data processed by the cloud-based services. In other embodiments, the remote device  1410  may be used to request information relating to one or more of the devices  1402 ,  1404 ,  1406  for analysis by the user. The remote device  1410  may be configured to access any functions of the cloud-based service  1408 , for which the remote device  1410  has sufficient permissions. 
       FIG. 15  is a block diagram illustrating a de-centralized IoT environment  1500 , according to some embodiments. Similar to environment  1400  described above, the environment  1500  includes a number of devices  1502 ,  1504 ,  1506 . In one embodiment, the devices  1502 ,  1504 ,  1506  are similar to devices  1402 ,  1404 ,  1406 , described above. The environment  1500  may further include a cloud-based service  1508  and a remote device  1510 . The cloud-based service  1508  and the remote device  1510  may function as cloud-based service  1408  and remote device  1410  described above. The environment  1500  is further shown to include a collator  1512 . 
     The devices  1502 ,  1504 ,  1506  may be configured to communicate between each other, or to the cloud-based service  1508  via the collator  1512 . In one embodiment, the devices  1502 ,  1504 ,  1506  are configured to communicate with each other over a network, such as BACnet. However, other networks, such as local-area-networks (LAN), wide-area networks (WAN), TCP/IP or other networks are also included. In some embodiments, the devices  1502 ,  1504 ,  1506  may communicate with each other via a wireless protocol, such as Wi-Fi, LoRa, Cellular (3G, 4G, CDMA, LTE), Wi-Max, Bluetooth, Zigbee, etc. The devices  1502 ,  1504 ,  1506  may include one or more processors, such as a microprocessor capable of processing instructions. The devices  1502 ,  1504 ,  1506  may be configured to process data within each device  1502 ,  1504 ,  1506 . The devices  1502 ,  1504 ,  1506  may further be configured to receive one or more instructions from the cloud-based service  1508 . For example, the cloud-based service  1508  may instruct the devices  1502 ,  1504 ,  1506  to perform certain actions, or to provide specific data to the cloud-based service  1508 . In some embodiments, the devices  1502 ,  1504 ,  1506  may receive the requests from the cloud-based service and communicate with each other to provide the requested service. 
     In some embodiments, the devices  1502 ,  1504 ,  1506  communicate with the cloud-based service  1508  via the collator  1512 . The collator  1512  is configured to provide coordination between the devices  1502 ,  1504 ,  1506 . In some embodiments, the collator  1512  may be a software element within a local device, such as an internet gateway (not shown). In other embodiments, the collator  1512  may be a service within the cloud-based services  1508 . The collator  1512  may be configured to facilitate Edge computing between the devices  1502 ,  1504 ,  1506 . For example, the collator  1512  may be configured to coordinate between the device  1502 ,  1504 ,  1506  to provide instructions to facilitate Edge computing (e.g. peer to peer or mesh computing). Further, the collator  1512  may serve to organize data received from multiple devices  1502 ,  1504 ,  1506 . For example, the collator  15012  may be configured to provide the unified management and processing of IoT data described below. 
     Turning now to  FIG. 16 , a block diagram illustrating a multi-modal data processing service  1600  is shown, according to some embodiments. The multi-modal data processing service  1600  includes a timeseries microservice API  1602 , a processing layer  1604  and a storage layer  1606 . The timeseries microservice API  1602  may provide an interface between one or more devices, databases, controllers, or other source of data via the API. The timeseries microservice API  1602  may handle queries provided to the multi-modal data processing service  1600 , which are then served directly from the storage layer  1606 , ensuring low round-trip time (RTT). In some embodiments, the timeseries microservice API  1602  may route data to the proper layer within the multi-modal data processing service  1600  based on the type of data received. For example, telemetry data, or other data received from sensors or other devices may be routed to the processing layer  1604 . In other examples, previously stored data, such as data received from databases or other data storage types may be provided to the storage layer  1606 . In one embodiment, the previously stored data, or data reads, may be provided to the timeseries storage service API  1608  for processing into the storage layer  1606 . In one embodiment, the timeseries storage service API  1608  is configured to parse the data reads to determine how the data reads should be stored within the storage layer  1606 . 
     The storage layer  1606  may be configured to store multiple data types. In one embodiment, the storage layer  1606  includes a multi-modal data store  1610 . The multi-modal data store  1610  may store the different multi-modal data types. For example, the multi-modal data store  1610  may include a document store  1612 , a column store  1614 , a relational store  1616  and an events store  1618 . In some examples, the multi-modal data store  1610  may also include in-memory cache for quickly accessing recent items stored in a memory associated with the storage layer  1606  and/or the multi-modal data processing service  1600 . The data associated with the document store  1612 , the column store  1614 , the relational store  1616  and the events store  1618  will be described in more detail below. 
     The processing layer  1604  may be configured to process one or more data messages  1620  received by the multi-modal data processing service  1600 . data messages  1620  can include telemetry data from one or more sources, such as sensors, controllers, or other devices. The processing layer  1604  may receive one or more data messages  1620 . The data messages  1620  may be unpacked at process element  1622 . In one embodiment, the unpacked data is pushed to the storage layer  1606 . The storage layer  1606  may analyze the unpacked data to determine if additional information may be required to process the data message  1620 . The additional information may include metadata (e.g. device type, age, etc.), historical content tags (prior incidents of faults, service history, etc.) as well as the definitions of data aggregation and transformation operations that need to be performed on the data message  1620  for generating analytics. The definitions of data aggregation and transformation operations may include cleansing, filling, aggregations, windowing operations, etc.). The additional data may be accessed from the multi-modal data store  1610 . In one example, the additional data may be accessed from the multi-modal data store  1610  via the in-memory cache. 
     The data message  1620  is then combined with the additional information provided via the multi-modal data store  1610  to form enriched data message  1624 . In one embodiment, the additional information is combined with the data message  1620  at processing element  1626 . The processing layer  1604  may further include a processing service API  1628  and a multi-modal processing stack  1630 . The processing service API  1628  is configured to access one or more processing engines within the multi-modal processing stack  1630  to allow for the enriched data message  1624  to be processes. Example processing engines may include DotNet/C# engines, Python engines, SparkSQL engines, GraphX Engines, MLlib Engines, MATLAB engines, etc. The multi-modal processing stack  1630  is configured to perform the required operations to process the enriched data message  1624 . The multi-modal processing stack  1630  may further be able to generate metrics, such as transformed timeseries data, and other analytics. For example, the analytics may determine that a piece of equipment may be at a high risk of a safety shutdown within the next 24 hours. The metrics and analytics may then be stored in the storage layer  1606 . 
     The multi-modal data processing service  1600  is configured to manage and process heterogeneous data types and data models associated with an IoT environment. Example data types and data models may include timeseries data, 3D design data, graphical data, structure, unstructured, and/or semi-structured data, video data, audio data, and the like.  FIG. 17  illustrates an example of multi-modal information related to a building chiller system, and specifically to a predictive maintenance application related thereto. While the following examples, are described in relation to a chilling system and a predictive maintenance application, it is contemplated that the multi-modal data processing service  1600  is compatible with other equipment within a BMS, as well as non-BMS related equipment. The multi-modal data processing service  1600  is further compatible with other applications. Accordingly, the following examples are not intended to be limiting to a specific implementation. As stated above  FIG. 17  is an example user-interface  1700  providing a view of multi-modal data. The user-interface  1700  can be a highly efficient tool for providing information to users, allowing then to better understand causalities of events collected from various sensors or other data inputs within the BMS. For example, as it relates to a chilling system, the user-interface  1700  may include events collected from various sensors related to the chilling system, applications including service logs (e.g., technician notes), vibration analysis, oil analysis, cameras, ultrasound sensors, thermometers, weather stations, or other data inputs related to the chilling system. In one embodiment, the user-interface  1700  is generated by the multi-modal data processing service  1600 . In other embodiments, the user-interface  1700  may be generated by a cloud service, such as those described above, and viewed using a remote device. 
     The user-interface  1700  can include an equipment data portion  1702 . The equipment data portion  1702  can provide information related to the piece of equipment being evaluated. Equipment data may include equipment name, location, operating status, network address, and the like. The user-interface  1700  can further include a time period portion  1704 . The time period portion  1704  may be a user selectable time frame from which to view various data types and values related to the equipment. In one embodiment, the time period portion  1704  may reflect a set time length (e.g. ten minutes, one hour, one day, etc.). In other embodiments, the time period portion  1704  may be configured to display a certain time period. For example, a time period between one time (e.g. 12:00 AM) and a second time (e.g. 12:00 PM). In some examples, the time period portion  1704  can be configured to reflect any time frame requested by the user. In one embodiment, the time period portion  1704  is associated with a failure, repair, or other event associated with the associated equipment or system. 
     The user-interface  1700  may further be configured to display one or more multi-modal data points with respect to the time period portion  1704 . For example, the user-interface  1700  is shown to display technician images of components  1706 , a vibration analysis  1708 , an ultrasound analysis  1710 , a technician note  1712  and telemetry data  1714 . The technician images of components  1706  may be images of components that have experienced a failure, either recently or in the past. The technician images of components  1706  may include image files such as .jpeg, .gif, .raw, .bmp, or other applicable image files. In other examples, the technician images of components  1706  may be video files. The vibration analysis  1708  may be an audio file, such as .mp3, .wav, .aiff, .wma, or the like. The vibration analysis  1708  may also include a visual representation of the audio file, such as a spectrum analysis for illustrating specific frequencies detected during the vibration analysis. The ultrasound analysis  1710  may include an audio file or an image file to illustrate the results of the ultrasound analysis  1710 . In some embodiments, the ultrasound analysis  1710  may include data in a tabular format, such as in a .csv, or .xls file for export and manipulation by a user. The technician note  1712  may be a textual note, or an audio note. In some embodiments, the technician note  1712  may be an annotated image or other file type. The telemetry data  1714  may be present for one or more sensors associated with the equipment. In some embodiments, the telemetry data is presented in a visual form, such as the graph shown in  FIG. 16 . However, in other embodiments, the telemetry data may be provided in other forms, such as via a spreadsheet (e.g. .csv, .xls). The above examples are exemplary only, and it is contemplated that the user-interface  1700  can display multiple different types of multi-modal data, as relevant for a particular piece of equipment. 
     Each of the images of components  1706 , the vibration analysis  1708 , the ultrasound analysis  1710 , the technician note  1712  and the telemetry data  1714  have one or more reference points on the time period portion  1704 . For example, the telemetry data  1714  shows telemetry data associated with the entire time period displayed on the time period portion  1704 , while the other multi-modal data items have discrete points on the time period portion  1704 . For example, the technician images of components  1706  are associated with a discrete time, while the ultrasound analysis  1710  is associated with a second time. Thus, the user-interface  1700  provides a unified timeline visualization of failure, repair and operation, failure and other related events, and a telemetry data stream to a user, in this example. By unifying multiple data points and types associated with a piece of equipment of a system, an accurate and detailed history of one or more attributes of the equipment or system can easily be presented to a user for analysis. 
     This multitude of varied data types and data models can introduce a set of challenges as it relates to storing and indexing the varied data types and data models to provide a comprehensive view as shown in user-interface  1700 . In one embodiment, multi-modal data processing service  1600  may be configured to use a polyglot persistence approach to processing the data, which allows for the storage of heterogeneous data types and other data models using multiple data storage technologies. The multiple storage technologies chosen based upon the way data is being used by individual applications or components of a single application. Using polyglot persistence, the multi-modal data processing service  1600  is responsible for providing Atomicity, Consistency, Isolation, and Durability (ACID) among different data models and storages. 
     Turning now to  FIG. 18 , a block diagram illustrating an IoT application storage topology  1800  is shown, according to some embodiments. The IoT application storage topology  1800  may include multiple storage technologies for use with polyglot persistence methods, described above. The iot application storage topology  1800  may include document storage  1802 , events storage  1804 , entity relationship storage  1806 , and report storage  1808 . The document storage  1802  may include a document database  1810 . The document database  1810  can be used to store completed service histories, and maintenance records, as well as static and dynamic relationships among entities including owner information, locations, asset details, and other maintenance recommendations. 
     The events storage  1804  can include a key value store  1812 . The key value store  1812  can be used to store maintenance and repair events, as well as service recommendations (e.g. result of predictive analytics). The entity relationship storage  1806  may include a graph store  1814 . The graph store  1814  may include results of predictive analytics performed by the multi-modal data processing service  1600 . For example, the graph store  1814  may include model results of the predictive analytic data. The reports storage  1808  may include a relational database  1816 . Within an application, such as the exemplary predictive maintenance application described above, application data can be modeled with JavaScript Object Notation (JSON) like semi-structured objects or structured entities that can be efficiently stored and queried within one or more relational databases  1816 . Example, data stored within the relational databases  1816  may include descriptions of installed locations of an asset, owner information details, product specifications, firmware versions, telemetry data points, etc. In one embodiment, the document database  1810 , the key value stores  1812 , the graph store  1814  and the relational database  1816  are stored in the multi-modal data store  1610  of the multi-modal data processing service  1600 . In other embodiments, one or more of the document database  1810 , the key value stores  1812 , the graph store  1814  and the relational database  1816  are located in a cloud, such as cloud-based services  1408 ,  1508 . 
     As the multi-modal data processing service  1600  learns and discovers more about relationships between events and entities, the multi-modal data processing service  1600  is configured to consistently introduce new relationships, and update or delete existing relationships through analytics services, (i.e., enriching semantic relationships). For example, a newly added maintenance event may lower a future failure mode of an asset by updating a causal relationship between the asset and a failure type. A set of recommended maintenance services (e.g. a set of entities) can be introduced to an asset by creating or updating a relationship between an asset and a service. 
     Data Models for a Predictive Maintenance Application 
     Returning now to the predictive maintenance example, the multi-modal data processing service  1600  may model the chiller with a digital twin that is a virtual representation of a physical device, there the digital twin is a computerized companion of the physical device (e.g. the chiller system for purposes of this example). The digital twin may be a 3D cad model with product specifications, or a set of telemetry data points associated to the physical device. In one embodiment, the data model representing the digital twin is a document (e.g., a JSON-based document), that can be managed via document database  1810 . In one embodiment, the document database  1810  may manage the documents using document stores such as MongoDB or DocumentDB. In some embodiments, the multi-modal data processing service  1600  may include a back-end service to ensure state consistency between a physical device and a device twin. The entity relationship storage  1806  may include a set of application specific or business data, including a location of an asset, a product operating specification, an owner information of assets, an organizational hierarchy of assets, service provider details, and/or other information required to perform predictive field services. In some examples, entity relationship modeling is useful where entities can be stored in a relational database (e.g. relational database  1816 ) or a document database (e.g. document database  1810 ) where semantics between entities must be handled by an application. Graph databases, such as graph store  1814  may also be used to model dynamic relationships between entities. 
     Data Management in a Predictive Maintenance Application 
     A connected device, such as a chiller, generates many different types of streaming data, including sensor readings, click streams, etc. Thus, data management and processing are an essential part of an IoT system. As described above, a variety of data types may be presented to the multi-modal data processing service  1600  in a predictive maintenance application (or other relevant application). For example, every service event can generate relevant data for future operational optimizations. For example, maintenance service events can include various multimedia data points, including textual reports on oil analysis (e.g. .pdf, .doc, or other document type), raw vibrational data, images of failed components, 3D models of the device and repair parts, technician service notes, ultrasound data, and the like. In one example, a picture of a degraded component can be uploaded to one or more cloud services for a condition assessment. For example, the cloud service may be an advanced image analysis service. If a replacement part is determined to be required, the cloud service will place a replacement part order and a work order. In one embodiment, the cloud service is one or more service accessed by the multi-modal processing stack  1630 . In other embodiments, the multi-modal data processing service  1600  is the cloud service responsible for coordinating the analysis. 
     Turning now to  FIG. 19 , a block diagram illustrating of a data scheme  1900  associated with a piece of equipment  1902 , such as chiller is shown, according to some embodiments. The equipment  1902  may have a number of associated data points associated with the equipment  1902 . For example, the equipment  1902  may be associated with maintenance logs  1904 , service histories  1906 , reliability analysis  1908 , product manuals/specifications  1910 , telemetry data  1912 , device shadows  1914 , service parts  1916 , building/installation profiles  1918 , user profiles  1920 , or other data points. The data points may include multiple data types, as described below in Table 1. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Multimedia Data Types and Associated Usage Examples 
               
            
           
           
               
               
               
            
               
                   
                 Data Type 
                 Usage Example 
               
               
                   
                   
               
               
                   
                 Image 
                 Picture of faulty parts, asset 
               
               
                   
                   
                 image, condition audit 
               
               
                   
                 PDF/Scanned Document 
                 Product specifications, manual, 
               
               
                   
                   
                 service history 
               
               
                   
                 Unstructured Text 
                 service note, customer&#39;s problem 
               
               
                   
                   
                 description 
               
               
                   
                 Structured/Semi-structured 
                 Application metadata, user profile, 
               
               
                   
                   
                 business transaction data, etc. 
               
               
                   
                 Time series, events 
                 Vibration analysis, faults, sensor 
               
               
                   
                   
                 readings, safety alerts, etc. 
               
               
                   
                 Video 
                 Repair sequence instructions, 
               
               
                   
                   
                 operating instructions, etc. 
               
               
                   
                 Audio 
                 Mechanical rotating device 
               
               
                   
                   
                 operating samples, operating 
               
               
                   
                   
                 environment noise, etc. 
               
               
                   
                   
               
            
           
         
       
     
     The data points shown in  FIG. 19  may also provide various metadata points to the multi-modal data processing service  1600 . Example metadata may include data capture locations, author, time of capture, target asset, etc. The metadata points provide contextual content for analysis and data processing of the multi-modal data. The above data points and associated metadata may be stored in storage layer  1606  of the multi-modal data processing service  1600 , or other databases accessible by the multi-modal data processing service  1600 . For example, the data points and metadata may be stored using blob storage, files systems, databases, etc. 
     The multi-modal data processing service  1600  may be configured to store, index and query various data models described above, including documents, graphs, and events. In one embodiment, the multi-modal data processing service  1600  accesses a predictive maintenance analytic service to provide a predictive maintenance analysis. The predictive maintenance analytic service may be accessed via the processing service API  1628 . In one embodiment, the predictive maintenance analytic service access one or more multi-modal data stores within the multi-modal data store  1610 . The predictive maintenance analytic service may access the stores to find all relevant measurement identifiers to a target asset, timeseries data, and events to create a data frame for analysis. In some examples, the telemetry data is stored in a timeseries store, which may utilize different storage technology. 
     The predictive maintenance analytic service may apply predictive failure analytics (e.g., matched potential failures and service recommendations. The predictive maintenance analytic service may further examine one or more data frames to determine when an asset may failed. The predictive maintenance analytic service may generate tagged events and update asset condition attributes illustrating high risks of failure of assets. The predictive maintenance analytic service may provide persisting analytic outcomes into a separate timeseries stream and add or update a tag in an entity to allow for more efficient future causality analysis. 
     Unified Data Management and Processing 
     As described above, the multi-modal data processing service  1600  may utilize polyglot persistence topologies to generate mapping between data points and types to provide strong consistency of data stored in two different data store. Specifically, polyglot persistence topology is used to map data between entity stores and telemetry data stores. Turning now to  FIG. 20 , a data map  2000  illustrating data mapping between entity/document stores and streamed data (e.g. telemetry data) stores, according to some embodiments.  FIG. 20  has an application layer  2002 . The application layer  2002  may be configured to map data between a document store/event store/graph store  2004  and a columnar store  2006  (e.g., time series store). The application layer  2002  may utilize one or more identifiers  2008  associated with data points within the document store/event store/graph store  2004 , and one or more identifiers  2010  associated with data points within the columnar store  2006 , to map data points in the document store/event store/graph store  2004  to the columnar store  2006 . The application layer  2002  is further responsible for maintaining ACID properties between the different storage technologies (e.g. the document store/event store/graph store  2004  and the columnar store  2006 ). 
     The mapping used in  FIG. 20  can require maintaining mappings and building custom ACID services for each application, which can be expensive and tedious to maintain. These issues can be resolved by building a set of abstractions that provide APIs for application developers and data management applications. For example, a reference architecture  2100  is shown in  FIG. 21 . The architecture  2100  may allow various data storage technologies to be abstracted using storage I/O abstraction that provides consistent Create, Read, Update and Delete (CRUD) operations across multiple storage technologies. The architecture  2100  may include an application layer  2102 . The application layer  2102  can provide an API for accessing the architecture  2100 . The architecture may further include a knowledge management module  2104 , an ACID management module  2106 , an entity management module  2108 , a multimedia data and stream management module  2110 , an analytic services module  2112 , a database/storage/IO Abstraction module  2114 , a relational database management systems (RDBMS) module  2116 , a document store  2118 , a column-oriented storage  2120 , a key-value module  2122 , a graph store  2124  and a file and blob storage  2126 . The architecture  2100  may further include a security module  2128  for providing various security functions to the architecture  2100 . Finally, the architecture may include a management module  2130  for managing the various elements of the architecture, described above. 
     The knowledge management module  2104  is configured to store and maintain various knowledge based elements associated with a system or a device. The ACID management module  2106  is configured to maintain consistency among entities, attributes of entities, events, and/or telemetry data. The ACID management module  2106  is further configured to trigger consistency check services when certain data changes are determined, and to make updates to other storages (e.g., foreign key relationships among different data store), such as document store  2118 , column-oriented storage  2120 , key-value store  2122 , graph store  2124 , and file and blob storage  2126 . The entity management module  2108  provides master data service on stored entities and unified CRUD operations via storage abstraction APIs. The multimedia data &amp; stream management  2110  provides similar functionality of the entity management module  2108  and also processes various media types, blobs and files. The analytic services module  2112  is configured to provide timeseries analysis, image analysis, and other IoT data processing services. The database/storage/io abstraction module  2114  can manage the data stored within the various storage modules, as well as the underlying I/O abstractions relating to what data received from a device or system is associated with which storage module. The architecture  2100  removes the need to maintain mappings, and the requirements to interact with various low-level storage interfaces. 
     Turning now to  FIG. 22 , a flow chart illustrating a process  2200  for performing unified stream processing is shown, according to some embodiments. In one embodiment, the process  2200  is performed using the multi-modal data processing service  1600 . However, other cloud-based services may also perform process  2200 . At process block  2202 , telemetry data is received by a service, such as the multi-modal data processing service  1600 . In one embodiment, the telemetry data is provided by one or more sensors associated with a system or individual equipment. In some embodiments, the service receives all telemetry data in real time. In other embodiments, the service receives the telemetry data periodically. In one embodiment, the telemetry data is received by the service via one or more APIs. 
     At process block  2204 , the data message is unpacked. Unpacking the data message may include extracting all data types from the data message. For example, the telemetry data may be extracted, along with any metadata associated with the telemetry data. Once the data is unpacked, the unpacked data is transmitted to the storage services at process block  2206 . Storage services may include the multi-modal data stores  1610 , described above. The storage services then examine the unpacked to data to determine what, if any, additional data is required to process the message at process block  2208 . Additional data may include metadata (e.g. equipment type, age, etc.), historical content tags (e.g. prior incidents of faults, service history, etc.) as well as the definitions of data aggregation and transformation operations that need to be performed on the data to generate analytics (e.g. cleansing, filling, aggregations, windowing operations, etc.). 
     Once the additional data has been determined, the additional required data is fetched from one or more data stores (e.g. multi-modal data store  1610 ) at process block  2210 , and the data message is enriched with the additional data at process block  2212 . At process block  2214  the enriched data message is sent to one or more processing services to be processed. The processing services can perform the required operations and generate metrics (e.g. transformed time series data) and analytics (e.g. tags indicating certain determined attributes of the equipment or system. In one embodiment, the processing services may be DotNet C# processing engines, python engines, SparkSQL engines, GraphX engines, MLlib engines, or he like. 
     CONFIGURATION OF EXEMPLARY EMBODIMENTS 
     The construction and arrangement of the systems and methods as shown in the various exemplary embodiments are illustrative only. Although only a few embodiments have been described in detail in this disclosure, many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters, mounting arrangements, use of materials, colors, orientations, etc.). For example, the position of elements can be reversed or otherwise varied and the nature or number of discrete elements or positions can be altered or varied. Accordingly, all such modifications are intended to be included within the scope of the present disclosure. The order or sequence of any process or method steps can be varied or re-sequenced according to alternative embodiments. Other substitutions, modifications, changes, and omissions can be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present disclosure. 
     The present disclosure contemplates methods, systems and program products on any machine-readable media for accomplishing various operations. The embodiments of the present disclosure can be implemented using existing computer processors, or by a special purpose computer processor for an appropriate system, incorporated for this or another purpose, or by a hardwired system. Embodiments within the scope of the present disclosure include program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available media that can be accessed by a general purpose or special purpose computer or other machine with a processor. By way of example, such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code in the form of machine-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer or other machine with a processor. Combinations of the above are also included within the scope of machine-readable media. Machine-executable instructions include, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing machines to perform a certain function or group of functions. 
     Although the figures show a specific order of method steps, the order of the steps may differ from what is depicted. Also two or more steps can be performed concurrently or with partial concurrence. Such variation will depend on the software and hardware systems chosen and on designer choice. All such variations are within the scope of the disclosure. Likewise, software implementations could be accomplished with standard programming techniques with rule based logic and other logic to accomplish the various connection steps, processing steps, comparison steps and decision steps.