Patent Publication Number: US-2022237157-A1

Title: Multimodal data reduction agent for high density data in iiot applications

Description:
BACKGROUND 
     The subject matter disclosed herein relates generally to industrial automation, and, more particularly, collection of industrial data. 
     BRIEF DESCRIPTION 
     The following presents a simplified summary in order to provide a basic understanding of some aspects described herein. This summary is not an extensive overview nor is intended to identify key/critical elements or to delineate the scope of the various aspects described herein. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later. 
     In one or more embodiments, a node system is provided, comprising a data input component configured to receive a data batch comprising one or more time-series values of a data tag of an industrial device; a modal analysis component configured to select a data reduction algorithm, from multiple predefined data reduction algorithms, based on a number of modes that occur in a probability distribution of the time-series values; and a data reduction component configured to apply the data reduction algorithm to the data batch to yield a reduced data set. 
     Also, one or more embodiments provide a method, comprising receiving, by a system comprising a processor, a data batch comprising one or more time-series values of a data tag of an industrial device; selecting, by the system based on a number of nodes detected in a probability distribution of the time-series data, a data reduction algorithm from multiple predefined data reduction algorithms; and applying, by the system, the data reduction algorithm to the data batch to yield a reduced data set. 
     Also, according to one or more embodiments, a non-transitory computer-readable medium is provided having stored thereon instructions that, in response to execution, cause a system comprising a processor to perform operations, the operations comprising receiving a data batch comprising one or more time-series values of a data tag of an industrial device; selecting, based on a number of nodes detected in a probability distribution of the time-series data, a data reduction algorithm from multiple predefined data reduction algorithms; and applying the data reduction algorithm to the data batch to yield a reduced data set. 
     To the accomplishment of the foregoing and related ends, certain illustrative aspects are described herein in connection with the following description and the annexed drawings. These aspects are indicative of various ways which can be practiced, all of which are intended to be covered herein. Other advantages and novel features may become apparent from the following detailed description when considered in conjunction with the drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram illustrating an example IIoT data pipeline. 
         FIG. 2  illustrates a high-level overview of an architecture in which data from an industrial enterprise can be migrated to a cloud platform. 
         FIG. 3  is a block diagram of an example pipeline node system. 
         FIG. 4  is a diagram illustrating movement of data across nodes of an IIoT data pipeline. 
         FIG. 5 a    is an example set of time-series data collected from a data tag of an industrial device, which can be included as part of a data batch being processed by a node system of an IIoT data pipeline. 
         FIG. 5 b    is a graph of the time-series data depicted in  FIG. 5   a.    
         FIG. 6 a    is a graph representing a unimodal probability distribution. 
         FIG. 6 b    is a graph representing a bimodal probability distribution. 
         FIG. 7  is a diagram illustrating an example data reduction process that can be carried out by a node system. 
         FIG. 8  is a diagram illustrating application of a No Change reduction strategy. 
         FIG. 9  is a graph of example time-series values of a data tag that invokes a No Change data reduction strategy. 
         FIG. 10  is a table of raw values of a data tag and a table of the reduced data set after a No Change reduction strategy is applied. 
         FIG. 11  is a diagram illustrating application of a Small Change reduction strategy. 
         FIG. 12  is a graph of example time-series values of a data tag that invokes a Small Change data reduction strategy. 
         FIG. 13  is a table of example raw values of a data tag and a table of the reduced data set after a Small Change reduction strategy is applied. 
         FIG. 14  is a diagram illustrating application of a Unimodal reduction strategy. 
         FIG. 15  is a bar chart of a probability distribution for an example set of raw data. 
         FIG. 16  is a table of example raw values of a data tag and a table of the reduced data set after a Unimodal reduction strategy is applied. 
         FIG. 17  is a diagram illustrating application of a Multimodal reduction strategy. 
         FIG. 18  is a bar chart of a probability distribution for an example set of raw data having two modes (a bimodal distribution). 
         FIG. 19  is a table of example raw values of a data tag and a table of the reduced data set after a Multimodal reduction strategy is applied. 
         FIG. 20  is a diagram illustrating application of a No Mode reduction strategy. 
         FIG. 21  is a table of example raw values of a data tag and a table of the reduced data set after a No Mode reduction strategy is applied. 
         FIG. 22  depicts a table representing an example set of raw data and a table depicting a reduced data set that has been generated based on a selected data reduction strategy. 
         FIG. 23  is a diagram illustrating an example IIoT data pipeline architecture that includes at least one pipeline node system as part of the pipeline backbone. 
         FIG. 24  is a screenshot of an example graph that can be rendered by a visualization application based on a reduced data set. 
         FIG. 25  is an example detail screen populated by raw industrial data. 
         FIG. 26 a    is a flowchart of a first part of an example methodology for generating a reduced data batch from a raw data batch comprising industrial data collected from industrial devices on a plant floor. 
         FIG. 26 b    is a flowchart of a second part of the example methodology for generating a reduced data batch from a raw data batch comprising industrial data collected from industrial devices on a plant floor. 
         FIG. 26 c    is a flowchart of a third part of the example methodology for generating a reduced data batch from a raw data batch comprising industrial data collected from industrial devices on a plant floor. 
         FIG. 26 d    is a flowchart of a fourth part of the example methodology for generating a reduced data batch from a raw data batch comprising industrial data collected from industrial devices on a plant floor. 
         FIG. 26 e    is a flowchart of a fifth part of the example methodology for generating a reduced data batch from a raw data batch comprising industrial data collected from industrial devices on a plant floor. 
         FIG. 27  is an example computing environment. 
         FIG. 28  is an example networking environment. 
     
    
    
     DETAILED DESCRIPTION 
     The subject disclosure is now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding thereof. It may be evident, however, that the subject disclosure can be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate a description thereof. 
     As used in this application, the terms “component,” “system,” “platform,” “layer,” “controller,” “terminal,” “station,” “node,” “interface” are intended to refer to a computer-related entity or an entity related to, or that is part of, an operational apparatus with one or more specific functionalities, wherein such entities can be either hardware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to being, a process running on a processor, a processor, a hard disk drive, multiple storage drives (of optical or magnetic storage medium) including affixed (e.g., screwed or bolted) or removable affixed solid-state storage drives; an object; an executable; a thread of execution; a computer-executable program, and/or a computer. By way of illustration, both an application running on a server and the server can be a component. One or more components can reside within a process and/or thread of execution, and a component can be localized on one computer and/or distributed between two or more computers. Also, components as described herein can execute from various computer readable storage media having various data structures stored thereon. The components may communicate via local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the Internet with other systems via the signal). As another example, a component can be an apparatus with specific functionality provided by mechanical parts operated by electric or electronic circuitry which is operated by a software or a firmware application executed by a processor, wherein the processor can be internal or external to the apparatus and executes at least a part of the software or firmware application. As yet another example, a component can be an apparatus that provides specific functionality through electronic components without mechanical parts, the electronic components can include a processor therein to execute software or firmware that provides at least in part the functionality of the electronic components. As further yet another example, interface(s) can include input/output (I/O) components as well as associated processor, application, or Application Programming Interface (API) components. While the foregoing examples are directed to aspects of a component, the exemplified aspects or features also apply to a system, platform, interface, layer, controller, terminal, and the like. 
     As used herein, the terms “to infer” and “inference” refer generally to the process of reasoning about or inferring states of the system, environment, and/or user from a set of observations as captured via events and/or data. Inference can be employed to identify a specific context or action, or can generate a probability distribution over states, for example. The inference can be probabilistic—that is, the computation of a probability distribution over states of interest based on a consideration of data and events. Inference can also refer to techniques employed for composing higher-level events from a set of events and/or data. Such inference results in the construction of new events or actions from a set of observed events and/or stored event data, whether or not the events are correlated in close temporal proximity, and whether the events and data come from one or several event and data sources. 
     In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless specified otherwise, or clear from the context, the phrase “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, the phrase “X employs A or B” is satisfied by any of the following instances: X employs A; X employs B; or X employs both A and B. In addition, the articles “a” and “an” as used in this application and the appended claims should generally be construed to mean “one or more” unless specified otherwise or clear from the context to be directed to a singular form. 
     Furthermore, the term “set” as employed herein excludes the empty set; e.g., the set with no elements therein. Thus, a “set” in the subject disclosure includes one or more elements or entities. As an illustration, a set of controllers includes one or more controllers; a set of data resources includes one or more data resources; etc. Likewise, the term “group” as utilized herein refers to a collection of one or more entities; e.g., a group of nodes refers to one or more nodes. 
     Various aspects or features will be presented in terms of systems that may include a number of devices, components, modules, and the like. It is to be understood and appreciated that the various systems may include additional devices, components, modules, etc. and/or may not include all of the devices, components, modules etc. discussed in connection with the figures. A combination of these approaches also can be used. 
     Industrial controllers and their associated I/O devices are central to the operation of modern automation systems. These controllers interact with field devices on the plant floor to control automated processes relating to such objectives as product manufacture, material handling, batch processing, supervisory control, and other such applications. Industrial controllers store and execute user-defined control programs to effect decision-making in connection with the controlled process. Such programs can include, but are not limited to, ladder logic, sequential function charts, function block diagrams, structured text, or other such programming structures. 
     Because of the large number of system variables that must be monitored and controlled in near real-time, industrial automation systems often generate vast amounts of near real-time data. In addition to production statistics, data relating to machine health, alarm statuses, operator feedback, electrical or mechanical load over time, and the like are often monitored, and in some cases recorded, on a continuous basis. This data is generated by the many industrial devices that make up a typical automation system, including the industrial controller and its associated I/O, telemetry devices for near real-time metering, motion control devices (e.g., drives for controlling the motors that make up a motion system), visualization applications, lot traceability systems (e.g., barcode tracking), etc. Moreover, since many industrial facilities operate on a 24-hour basis, their associated automation systems can generate a vast amount of potentially useful data at high rates. The amount of generated automation data further increases as additional plant facilities are added to an industrial enterprise. 
     To gain insights into the operation of plant-floor automation systems and processes, this high-density industrial data can be collected and streamed to analytics, visualization, or reporting systems residing on a cloud platform or another high-level platform via a data pipeline, or a network of parallel data pipelines, as part of an industrial internet of things (IIoT) architecture.  FIG. 1  is a diagram illustrating an example IIoT data pipeline. Data pipeline  114  can comprise a series of chained nodes  104  capable of relaying aggregated industrial data  102  from an edge device  106  residing on the plant floor to cloud storage  110 . Nodes  104  may be server devices, microservices executing on respective computer hardware platforms, or other such processing elements. Each of the chained nodes  104  receives data from an adjacent upstream node  104  and passes this data to an adjacent downstream node  104  to thereby convey data from the source to the destination. In some architectures, a sending node can be connected to multiple upstream feeder nodes and/or multiple downstream receiving nodes. In addition to conveying collected industrial data through the pipeline  114 , a given node  104  may also perform processing on received data in accordance with a data processing application installed on the node  104 . Such application may include, but are not limited to, notification applications that generate and send notifications to specified client devices if any of the data satisfies a defined notification criterion, data transformation applications that transform or reformat the data to suit the needs of a target application, or other such applications. 
     Any of the nodes  104  or edge device  106  may perform processing on the collected data  102  as the data is streaming through the pipeline  114 , and as such the data  108  that is delivered to the cloud storage  110  may be a processed version of the data  102  collected from the plant floor devices and sensors that make up the plant floor automation systems. Once the data  108  has been moved to cloud storage  110 , the data  108  can be analyzed or visualized by high-level applications  112 . 
     Many IIoT applications convey variable data volumes that must be organized for reporting or analysis purposes. When time-series data on the plant floor is generated at a high-speed rate, the large volume of data that is streamed through the pipeline  114  can cause problems in the cloud-based applications due to the large data point density. For example, processing large volumes of data can increase the processing latency of the cloud-side applications, which may result in data congestion within the pipeline  114 , or may overload those applications and necessitate a system restart. In the case of visualization applications, such as cloud-based HMIs, the total volume of raw data generated by the plant-floor industrial devices may be too large and noisy to render a clear visualization that can be easily interpreted by a viewer, and as such visualizing the raw data in its entirety can obscure important events or trends within the data. 
     To reduce the volume of data provided to the cloud-side applications, additional data pipeline backplane processing can be implemented to perform data reduction steps on the streaming data, resulting in a smaller data set that is more suitable for cloud-side visualization or reporting. However, arbitrary truncation of data using a simple truncation criterion cannot guaranteed data consistency and accuracy. With such approaches, there is a trade-off between high data reduction and data accuracy. As such, crude truncation approaches may reduce data volume but may also reduce the accuracy of reporting, analytic, or predictive applications that consume the data. Moreover, simple data truncation strategies do not maintain an associative link between the reduced data set and the original raw data, leaving no means for a user to easily access a selected set of the original raw data corresponding to a particular subset of the reduced data set in order to view higher-resolution data surrounding a selected point in time. 
     To address these and other issues, one or more embodiments described herein provide data reduction services that can be implemented in one or more nodes of an IIoT data pipeline to intelligently determine an appropriate data reduction strategy based on characteristics of the incoming data. In one or more embodiments, data reduction components on the pipeline node or on an edge device can define different data filtering rules or algorithms that can be selectively applied to a given set of streaming time-series data based on a probability distribution of the data. The data pipeline node can perform real-time distribution analysis on the streaming data to determine whether the data has a unimodal distribution, a multimodal distribution, or no mode, and select one of the data filtering rules based on this determined probability distribution. Additional filtering rules can also be defined for cases in which there is no change in the data or only small changes to the data within a given data set (e.g., a given batch of data being conveyed through the data pipeline  114 ). In this way, the data is intelligently reduced in a manner that retains critical information within the reduced data set while achieving a high level of data reduction. This approach can yield a reduced data set by identifying nominal data that is not associated with anomalies, thereby maintaining high accuracy. 
     Additionally, the data reduction services define linkages that associate items of the reduced data set with their corresponding sets of raw data, thereby creating a means to easily access the original higher-resolution data surrounding a selected item of the reduced data set. Thus, synchronization between the reduced data set and the corresponding raw data is maintained, allowing a user to easily navigate between lower-resolution visualization of the data to the higher-resolution raw data. 
     As noted above, the IIoT data reduction system described herein can be used as part of an IIoT data pipeline used to migrate data generated at one or more plant facilities to a cloud environment for storage, analysis, reporting, or visualization.  FIG. 2  illustrates a high-level overview of an architecture in which data from an industrial enterprise can be migrated to a cloud platform. This architecture is an example context in which embodiments of the reactive buffering system can be used. The enterprise comprises one or more industrial facilities  204 , each having a number of industrial devices  208  and  210  in use. The industrial devices  208  and  210  can make up one or more automation systems operating within the respective facilities  204 . Example automation systems can include, but are not limited to, batch control systems (e.g., mixing systems), continuous control systems (e.g., PID control systems), or discrete control systems. Industrial devices  208  and  210  can include such devices as industrial controllers (e.g., programmable logic controllers or other types of programmable automation controllers); field devices such as sensors and meters; motor drives; operator interfaces (e.g., human-machine interfaces, industrial monitors, graphic terminals, message displays, etc.); industrial robots, barcode markers and readers; vision system devices (e.g., vision cameras); safety relays, optical safety systems, or other such industrial devices. 
     Industrial automation systems can include one or more industrial controllers that facilitate monitoring and control of their respective processes. These industrial controllers exchange data with the field devices using native hardwired I/O or via a plant network such as Ethernet/IP, Data Highway Plus, ControlNet, Devicenet, or the like. A given controller typically receives any combination of digital or analog signals from the field devices indicating a current state of the devices and their associated processes (e.g., temperature, position, part presence or absence, fluid level, etc.), and executes a user-defined control program that performs automated decision-making for the controlled processes based on the received signals. The controller then outputs appropriate digital and/or analog control signaling to the field devices in accordance with the decisions made by the control program. These outputs can include device actuation signals, temperature or position control signals, operational commands to a machining or material handling robot, mixer control signals, motion control signals, and the like. The control program can comprise any suitable type of code used to process input signals read into the controller and to control output signals generated by the controller, including but not limited to ladder logic, sequential function charts, function block diagrams, structured text, or other such platforms. 
     Although the example architecture illustrated in  FIG. 2  depicts the industrial devices  208  and  210  as residing in fixed-location industrial facilities  204 , the industrial devices  208  and  210  may also be part of a mobile control application, such as a system contained in a truck or other service vehicle. 
     On-premise edge devices  106  can collect data from industrial devices  208  and  210 —or from other data sources, including but not limited to data historians, business-level systems, etc.—and feed this data into a data pipeline (e.g., pipeline  114  in  FIG. 1 ) which migrates the data to the cloud platform  202  for processing and storage. Cloud platform  202  can be any infrastructure that allows cloud services  212  to be accessed and utilized by cloud-capable devices. Cloud platform  202  can be a public cloud accessible via the Internet by devices having Internet connectivity and appropriate authorizations to utilize the services  212 . In some scenarios, cloud platform  202  can be provided by a cloud provider as a platform-as-a-service (PaaS), and the services  212  (e.g., data analysis, visualization, reporting, etc.) can reside and execute on the cloud platform  202  as a cloud-based service. In some such configurations, access to the cloud platform  202  and the services  212  can be provided to customers as a subscription service by an owner of the services  212 . Alternatively, cloud platform  202  can be a private or semi-private cloud operated internally by the enterprise, or a shared or corporate cloud environment. An exemplary private cloud can comprise a set of servers hosting the cloud services  212  and residing on a corporate network protected by a firewall. 
     Cloud services  212  can include, but are not limited to, data storage, data analysis, control applications (e.g., applications that can generate and deliver control instructions to industrial devices  208  and  210  based on analysis of real-time system data or other factors), automation system or process visualization applications (e.g., a cloud-based HMI), reporting applications, Enterprise Resource Planning (ERP) applications, notification services, or other such applications. Cloud platform  202  may also include one or more object models to facilitate data ingestion and processing in the cloud. 
     Ingestion of industrial device data in the cloud platform  202  can offer a number of advantages particular to industrial automation. For one, cloud-based storage offered by the cloud platform  202  can be easily scaled to accommodate the large quantities of data generated daily by an industrial enterprise. Moreover, multiple industrial facilities at different geographical locations can migrate their respective automation data to the cloud for aggregation, collation, collective analysis, visualization, and enterprise-level reporting without the need to establish a private network between the facilities. In another example application, cloud-based diagnostic applications can monitor the health of respective automation systems or their associated industrial devices across an entire plant, or across multiple industrial facilities that make up an enterprise. Cloud-based IIoT control applications can be used to track a unit of product through its stages of production and collect production data for each unit as it passes through each stage (e.g., barcode identifier, production statistics for each stage of production, quality test data, abnormal flags, etc.). Moreover, cloud-based control applications can perform remote decision-making for a controlled industrial system based on data collected in the cloud from the industrial system, and issue control commands to the system. These industrial cloud-computing applications are only intended to be exemplary, and the systems and methods described herein are not limited to these particular applications. The cloud platform  202  can allow software vendors to provide software as a service, removing the burden of software maintenance, upgrading, and backup from their customers. 
       FIG. 3  is a block diagram of an example pipeline node system  302  (also referred to herein simply as a node) according to one or more embodiments of this disclosure. Aspects of the systems, apparatuses, or processes explained in this disclosure can constitute machine-executable components embodied within machine(s), e.g., embodied in one or more computer-readable mediums (or media) associated with one or more machines. Such components, when executed by one or more machines, e.g., computer(s), computing device(s), automation device(s), virtual machine(s), etc., can cause the machine(s) to perform the operations described. 
     Node system  302 , which can be a node of an IIoT data pipeline having at least some of the functions of nodes  104  described above, can include a data input component  304 , a data output component  306 , a data averaging component  308 , a modal analysis component  310 , a data reduction component  312 , one or more processors  318 , and memory  320 . In various embodiments, one or more of the data input component  304 , data output component  306 , data averaging component  308 , modal analysis component  310 , data reduction component  312 , the one or more processors  318 , and memory  320  can be electrically and/or communicatively coupled to one another to perform one or more of the functions of the node system  302 . In some embodiments, components  304 ,  306 ,  308 ,  310 , and  312  can comprise software instructions stored on memory  320  and executed by processor(s)  318 . Node system  302  may also interact with other hardware and/or software components not depicted in  FIG. 3 . For example, processor(s)  318  may interact with one or more external user interface devices, such as a keyboard, a mouse, a display monitor, a touchscreen, or other such interface devices. 
     Data input component  304  can be configured to receive batches of data from an adjacent upstream node system of the data pipeline or from an edge device  106  (if the node system  302  is the first node of a data pipeline). Data output component  306  can be configured to send data—including both raw data received from the upstream node and a reduced version of the data—to an adjacent downstream node system of the data pipeline in data batches. 
     The data averaging component  308  can be configured to calculate, for each data tag of a current data batch, a weighted moving average of the time-series data values generated by the data tag. The modal analysis component  310  can be configured to analyze the values of the current batch of time-series data, as well as the weighted moving average calculated by the data averaging component  308 , to determine a relative amount of change and a probability distribution (e.g., unimodal, multimodal, no mode, etc.) of the data values for each data tag in the data batch. The data reduction component  312  can be configured to select a data reduction algorithm  322  from multiple defined reduction algorithms  322  based on the distribution determined by the modal analysis component  310 , and to apply the selected data reduction algorithm to the data batch to yield a reduced data set. 
     The one or more processors  318  can perform one or more of the functions described herein with reference to the systems and/or methods disclosed. Memory  320  can be a computer-readable storage medium storing computer-executable instructions and/or information for performing the functions described herein with reference to the systems and/or methods disclosed. 
     Although  FIG. 3  and examples described herein depict the data reduction functionality (e.g., components  304 ,  306 ,  308 ,  310 , and  312 ) as being embodied on a node of the data pipeline, in some embodiments the data reduction functionality can be embodied on an edge device (e.g., edge device  106 ) so that data reduction processing can be applied to the collected data  102  by the edge device prior to injecting the reduced data into the pipeline  114 . 
       FIG. 4  is a diagram illustrating movement of data across nodes of an IIoT data pipeline. In this example, pipeline node systems  302   11  and  302   22  are two adjacent nodes of an example IIoT data pipeline. When data is being sent from a node (i.e., node system  302 ) of a data pipeline to an adjacent downstream node, the node that is sending the data—e.g., node system S 11  in  FIG. 4 —is referred to as the reactive node, while the node that receives and acts on the data—e.g., node system S 22 —is referred to as the reduction node. Since nodes that are not at the extreme ends of the pipeline will both receive data from adjacent upstream nodes and send data to adjacent downstream nodes, these nodes act as both reactive and reduction nodes at various stages of their operation within a given data transmission cycle.  FIG. 4  also depicts a target node  406 , which is adjacent to and downstream from the reduction node S 22 . Target node  406  may be either another node system  302  of the pipeline, or a final storage destination for the data (e.g., a cloud-based storage node). As noted above, node systems  302  may be servers, micro-services, or other processing elements that form the backbone of the IIoT data pipeline. 
     In-motion data moves from node to node in micro-batches, or data batches. In some embodiments the size of these micro-batches is fixed. In other embodiments, the reactive node system  302   11  (S 11 ) can be capable of dynamically adjusting the size of its outgoing data batches  402  based on the processing latency experienced at the adjacent reduction node system  302   22  (S 22 ). The processing latency at the reduction node system  302   22  can be characterized by a set of time variables that quantify the times required to perform various tasks associated with processing and moving the data. 
     For example, T rac  represents the time required for the reactive node system  302   11  to collect the data that is to be included in the data batch  402  from an adjacent upstream data source (e.g., an upstream reactive node, one or more IIoT industrial devices, or an edge device  106 ). In general, this time to collect the data can be given as the product of the size N S  of the incoming data batch and the time T u  required for the reactive node system  302   11  to collect a record for a single data tag of the batch: 
         T   rac   =N   s   *T   u   (1)
 
     The time required for the reduction node system  302   22  to read the data batch  402  from the reactive node system  302   11  is given by T rdr . Upon reading the data batch  402 , the reduction node system  302   22  may apply processing to transform the data contained in the data batch  402 . In some applications this transformation may include applying data reduction processing to the data in order to reduce the data set. As will be discussed in more detail below, this can include detecting and removing anomalous data or outlier data, as well as applying a selected data reduction algorithm that filters the data based on an amount of change and a probability distribution of the data within the batch. In some scenarios, the reduction node system  302   22  may also process the incoming data batch  402  according to a node-specific application that executes on the node system (e.g., a notification application, an analytic application, etc.). The time required for the reduction node system  302   22  to reduce, process, or otherwise transform the data is given by T rdt . The time required for the reduction node system  302   22  to then write the resulting reduced data batch  404  to the target node  406  is represented by T rdw . The total time T rd  required for the reduction node system  302   22  to receive, process, and send the incoming data batch  402  can thus be given as 
         T   rd   =T   rdr   +T   rdt   +T   rdw   (2)
 
       FIG. 5 a    is a table  502  of an example set of time-series data collected from a data tag of an industrial device, which can be included as part of a data batch being processed by a node system  302  of an IIoT data pipeline.  FIG. 5 b    is a corresponding graph  508  of the time-series data tabulated in  FIG. 5 a   . Data  502  may be part of a data batch being processed by a node system  502 , and comprises data generated by a data tag of an industrial device (e.g., an industrial controller, a motor drive, a telemetry device, a sensor, etc.) on the plant floor. Although data from only a single data tag is depicted in  FIGS. 5 a  and 5 b   , a given data batch being processed by a node of an IIoT data pipeline may comprise different sets of time-series data collected from respective different data tags. Each data value  506  has an associated time stamp  504  indicating a time that the data value was generated by the corresponding plant floor device. For illustrative purposes, in addition to the time stamps and tag values that are typically included the time-series data, table  502  also includes columns for the mean value, median value, maximum value, and minimum value of the tag values. 
     Analysis of the time-series data contained in a batch can yield insights into the degrees of change and the key data values contained in the batch. With reference to  FIG. 5 b   , such analysis can include identification of local peaks and valleys within the data, maximum and minimum values, the mean value, and the median value. Based on these characteristics of the data  502 , the node system  302  can identify whether the time-series data  502  changes significantly over time, and if so, determine a mode of the data  502  based on a probability distribution. 
       FIGS. 6 a  and 6 b    are graphs  602  and  604  representing probability distributions for two example sets of data. The probability distribution represents a frequency of each value contained in the data set, or a number of times each value occurs in the data set. Based on this distribution, a data set can be characterized as having a mode if at least one data value has a local peak, which indicates that this data value has a predominance within the data set. Data sets that have such peaks—also referred to as modes—in their probability distributions can be further characterized as being unimodal if only a single peak is present, or multimodal if two or more peaks are present. Graph  602  depicts an example unimodal distribution having a single mode, while graph  604  depicts an example multimodal distribution having two peaks (a bimodal distribution). As will be described in more detail herein, the node system  302  analyzes each data set&#39;s probability distribution and, based on results of this analysis, selects a suitable data reduction rule or algorithm to apply to the data set to yield the reduced data set. 
       FIG. 7  is a diagram illustrating an example data reduction process that can be carried out by node system  302  according to one or more embodiments. Raw data  102  is received by the node system  302  via the data input component  304 . The raw data  102  can comprise a data batch sent by an adjacent upstream node or edge device of the data pipeline or, if the data reduction functionality is implemented on an edge device, may be raw data collected directly from data tags of one or more industrial devices on the plant floor. The raw data  102  may comprise, for each data tag, both time-series data values corresponding to a time range represented by the data batch as well as time stamps indicating a time that each of the time-series values was generated. Raw data  102  may also include tag information identifying the data tag from which the data was collected and other such metadata. 
     The raw data is provided to the data averaging component  308 , which determines, for each data tag represented in the batch of raw data  102 , a weighted moving average  704  for the data. The weighted moving average is used to determine whether the data values for the data tag vary by only a small amount, and therefore invoke a Small Change data reduction rule. The weighted moving average  704  and the raw data are then provided to the modal analysis component  310  which selects a data reduction algorithm  322  from a library of predefined reduction algorithms based on analysis of the weighted average  704  and the raw data. In general, the modal analysis component  310  selects a data reduction algorithm  322  to be applied to each tag&#39;s data based on a determination of whether the time-series values associated with the tag do not change across the time range represented by the raw data  102 , a determination of whether the values change by only a small degree, or a determination of the number of modes in the data&#39;s distribution. 
     Once a data reduction algorithm  322  has been selected, the modal analysis component  310  instructs the data reduction component  312  to apply the data reduction strategy  706  defined by the selected data reduction algorithm  322  to the raw data for the data tag. This results in a reduced data set  702  in which items of the raw data  102  considered non-essential are removed. As part of the data reduction process, the data reduction component  312  also maintains an associative link between each item of the reduced data set  702  and the corresponding values of raw data  102  that surround the data item. The data output component  306  then sends the reduced data set  702  to the next node of the pipeline, or to the data&#39;s final destination (e.g., a cloud-based visualization, reporting, or analytic application). Data output component  306  can also send the raw data  102  with the reduced data set  702 , together with the defined associations or linkages between the reduced data set  702  and the raw data  102 . 
     Example data reduction algorithms  322  that can be applied by embodiments of node system  302  for different data distribution scenarios are now described.  FIG. 8  is a diagram illustrating application of a No Change reduction strategy  706 . When raw data  102  is received by the node system  302 , the data input component  304  (not shown in  FIG. 8 ) may pre-process the data  102  in preparation for data reduction analysis. This may include, for example, removing outlier data that is likely to be invalid, sorting or ordering the data according to time stamps, removing non-numerical values, or performing other such pre-processing. 
     For a given data tag represented in the batch of raw data  102 , the modal analysis component  310  selects the No Change reduction strategy  706  from among the data reduction algorithms  322  in response to determining that the values for the data tag are equal for all the time stamps included in the data batch.  FIG. 9  is a graph  902  of example time-series values of a data tag represented in the raw data  102  that invokes a No Change data reduction strategy. As shown in this example, the data tag value remains consistent (V=4.5) across all time stamps. The modal analysis component  310  can use any suitable technique to determine whether the data values are equal for all time stamps; e.g., by determining whether the maximum data value is equal to the minimum value. 
     Since the data values remain unchanged for all time stamps included in the batch of raw data, it is considered necessary to send only a single data value from the raw data set as the reduced data set  702 . According to the No Change reduction strategy, the reduced data set  702  comprises a single data item or record including the value of the data tag and a time stamp selected from the midpoint, or an approximate midpoint, of the time range included in the data batch, as well as a data tag identifier.  FIG. 10  is a table  902  of the raw values of the data tag and a table  904  of the reduced data set  702  after the No Change reduction strategy is applied. In this example, the reduced data set represented by table  904  comprises only the data value and corresponding time stamp selected from the midpoint or approximate midpoint of the time range of the raw data (the data record highlighted in table  902 ). In this example, the No Change data reduction strategy reduces a batch of raw data  102  comprising 10 data points to a single data point. 
       FIG. 11  is a diagram illustrating application of a Small Change reduction strategy  706 . If the modal analysis component  310  determines that a data tag included in the raw data  102  does not invoke the No Change data reduction strategy, the weighted moving average  704  for the raw data  102  is examined. As noted above, for each data tag represented in the current batch of raw data  101 , the data averaging component  308  generates a weighted moving average  704  of the data values for that data tag. In some embodiments, the weighted average can be calculated based on an assumption that more recent data—representing more recent events detected on the plant floor—are of greater interest than older data and events. With this in mind, the weighted moving average can weigh more recent values of the data tag more heavily than older values. An example weighted average calculation that can be applied by the data averaging component  308  can be given by: 
       Average= W   1   *V   n−1   +W   2   *V   n−2   + . . . +W   k−1   *V   n−k+1   +W   k   *V   n−k   (3)
 
     where k is the number of data items or records in the data batch, W i  is a weighted coefficient between 0 and 1 for i=1 through k, V j  is the value of the data tag at a point in time T j  for j=1 through k, and n is an integer. Other approaches for calculating an average for the time-series data tag values are also within the scope of one or more embodiments. 
     In equation (3) the values of the weighted coefficients W i  are assumed to decrease as i increases, such that more recent values (e.g., V n−1 ) are weighed more heavily than less recent values (e.g., V n−k ). In an example scenario in which the data batch comprises five records (that is, k=5), the values of the weighted coefficients W, may be set as follows: 
     
       
         
           
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Example values of W i   
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 W 1   
                 0.3 
               
               
                   
                 W 2   
                 0.25 
               
               
                   
                 W 3   
                 0.2 
               
               
                   
                 W 4   
                 0.15 
               
               
                   
                 W 5   
                 0.1 
               
               
                   
                   
               
            
           
         
       
     
     The weighted moving average  704  for a data tag represented in the batch of raw data  102  can be used to determine whether the data tag value varies by only a small amount across the entire time range represented by the data batch. For example, a delta value representing the upper and lower bounds of a small change can be defined relative to the mean value of the data values, and each value V n  of the data tag can be compared with these upper and lower bounds to determine whether all values V are within the range. That is, for each value V n  of the data tag, the modal analysis component  310  can determine whether the following condition is satisfied: 
       | Vn −Average|&lt;Delta  (4)
 
     where Average is calculated based on equation (3) or another suitable formula for determining an average value. According to condition (4), the data values are assumed to satisfy the Small Change criterion if all the values are within a maximum deviation from the mean value. 
     If modal analysis component  310  determines that the absolute value of the difference between each value V n  of the data tag and the average value is less than the defined delta value, per condition (4), for all n values, the modal analysis component selects and applies the Small Change reduction strategy to the values.  FIG. 12  is a graph  1202  of example time-series values of a data tag represented in the raw data  102  that invokes a Small Change data reduction strategy. In this example, the time-series data oscillates around a mean value (represented by the horizontal grey line labeled Mean), which is calculated by the modal analysis component  310  using equation (3) or a variation thereof. The horizontal lines labeled Small Change Upper Bound and Small Change Lower Bound are offset from the Mean line by delta value in the positive and negative y-axis directions, and represent the delta value that determines whether the changes in the time-series data over time are small enough to be classified as a small change. If all values of the data tag remain between the Small Change Upper and Lower Bounds (that is, none of the tag values deviate from the mean in excess of the delta value), the modal analysis component  310  selects the Small Change reduction strategy, which is applied to the raw data  102  by the data reduction component  312 . According to the Small Change reduction strategy, the reduced data set  702  includes only the mean value of the data tag together with a time stamp selected from the midpoint or approximate midpoint of the time range, as well as a data tag identifier. 
       FIG. 13  is a table  1302  of example raw values of the data tag and a table  1304  of the reduced data set  702  after the Small Change reduction strategy is applied. For illustrative purposes, table  1302  also includes columns for the mean value (4.513 in this example) and the absolute value of the difference between each value V n  and the mean. Since each value of the absolute value of the difference between each tag value V n  and the mean is less than the defined delta (0.02 in this example), the mode analysis component applies the Small Change reduction strategy to the data, whereby the reduced data set  702  (represented by table  1304 ) comprises the mean value of the data (4.513) and a time stamp selected from the midpoint or approximate midpoint of the time range represented by the raw date  102 . For the illustrated data sample, this reduces the example data sample comprising ten data points to a single data point. 
       FIG. 14  is a diagram illustrating application of a Unimodal reduction strategy  706 . Modal analysis component  310  applies the Unimodal reduction strategy in response to determining that the probability distribution of the data values for the data tag vary in excess of the small change criterion discussed above, and that the distribution has only a single mode. To this end, modal analysis component  310  generates a probability distribution for each data tag in the raw data  102  and identifies the presence of modes within the distribution.  FIG. 15  is a bar chart  1502  of a probability distribution for an example set of raw data  102 . For a set of data comprising multiple distinct values, the distribution represents the number of occurrences, or frequency, of each distinct value in the raw data  102 . As noted above, a mode is a local peak within this distribution, indicating a value that occurs more frequently than its nearest neighbor values. In the example depicted in  FIG. 15 , a mode corresponding to the value V n =8 is present. This value occurs three times within the raw data  102 , more than any of the other values present in the raw data  102 . Since this is the only mode present in the data set, modal analysis component  310  selects the Unimodal data reduction strategy, which is applied to the raw data  102  by the data reduction component  312 . 
     According to the Unimodal data reduction strategy, the reduced data set  702  comprises the maximum value of the data set, the minimum value of the data set, and the mode, together with the time stamps associated with each of these values and a data tag identifier.  FIG. 16  is a table  1602  of example raw values of the data tag and a table  1604  of the reduced data set  702  after the Unimodal reduction strategy is applied. For illustrative purposes, table  1602  also includes columns for the mean, median, mode, maximum, and minimum values. As noted above, data records corresponding to the maximum value, minimum value, and mode value—highlighted in table  1602 —are selected for inclusion in the reduced data set  702  (represented by table  1604 ) together with their corresponding time stamps. Although the mode value occurs in the data set multiple times, only one of the data records corresponding to the mode value is selected. In some embodiments, the data reduction component  312  may select an instance of the mode value from the midpoint or approximate midpoint of the occurrences of the mode value, together with its corresponding time stamp, for inclusion in the reduced data set  702 . Alternatively, the data reduction component  312  may select the data record corresponding to the first instance in time at which the mode value occurs. Thus, the Unimodal strategy reduces the raw data  102  to three data points in the reduced data set  702 . 
       FIG. 17  is a diagram illustrating application of a Multimodal reduction strategy  706 . As in the example unimodal scenario described above, upon determining that the raw data  102  for a data tag does not invoke the No Change or Small Change data reduction strategies, the modal analysis component  310  generates a probability distribution of the raw data and identifies the modes of the resulting distribution.  FIG. 18  is a bar chart  1802  of a probability distribution for an example set of raw data  102  having two modes (a bimodal distribution). In this example, the modal analysis component identifies two modes, or local peaks, within the distribution, corresponding to values 3 and 8. In response to determining that more than one mode is present, the modal analysis component  310  selects the Multimodal data reduction strategy, and data reduction component  312  applies this strategy to the raw data  102 . 
     According to the Multimodal data reduction strategy, the reduced data set  702  comprises the maximum and minimum values of the raw data  102 , the values corresponding to each of the modes found in the raw data&#39;s probability distribution, the time stamps corresponding to each of these data values, and the data tag identifier.  FIG. 19  is a table  1902  of example raw values of the data tag and a table  1904  of the reduced data set  702  after the Multimodal reduction strategy is applied. For illustrative purposes, table  1902  also includes columns for the mean value, the median value, the most frequent mode value, the maximum value, and the minimum value. In this bimodal example, data reduction component  312  selects, for inclusion in the reduced data set  702 , the highlighted data records corresponding to the maximum value (12), the minimum value (1), and the values associated with the two modes (8 and 3), as well as the time stamps corresponding to each of these values. As in the Unimodal scenario, the mode values selected for inclusion in the reduced data set  702  can comprise the mode values in the midpoint or approximate midpoint of the range of corresponding mode values. Alternatively, the selected mode values may be the instances of the respective mode values that occur earliest in time. In this bimodal example, the reduced data set  702  (represented by table  1904 ) comprises four data points. However, if more than two modes are present, the reduced data set  702  will include additional data values so that each mode is represented. 
     The node system  302  may also define a data reduction strategy for scenarios in which the raw data  102  does not satisfy the No Change or Small Change criteria, but also does not have a mode.  FIG. 20  is a diagram illustrating application of such a No Mode reduction strategy  706 . In response to determining that the raw data  102  for a data tag does not invoke the No Change or Small Change data reduction strategies, the modal analysis component  310  generates a probability distribution of the raw data, as in the Unimodal and Multimodal scenarios. In this example, however, the modal analysis component  310  detects no mode within the resulting distribution. This may the case, for example, in sets of raw data  102  whose values vary constantly with no repeating values across the entire time range represented by the data batch, as in the example data set depicted in  FIGS. 5 a    and  5   b.    
     In response to determining that no modes are present in raw data&#39;s distribution, the modal analysis component  310  selects the No Mode data reduction strategy, and the data reduction component  312  applies the selected strategy to the raw data  102 . According to the No Mode reduction strategy, the reduced data set  702  comprises the maximum value, the minimum value, any local peak and/or local valley values contained in the raw data  102 , the time stamps corresponding to these values, and data tag identifier.  FIG. 21  is a table  2102  of example raw values of the data tag (with columns including the mean, median, maximum, and minimum values added for illustrative purposes) and a table  2104  of the reduced data set  702  after the No Mode reduction strategy is applied. The example raw data in this example corresponds to graph  508  illustrated in  FIG. 5 b   . In this example, the reduced data set  702  represented by table  2104  includes the maximum and minimum values (12 and 1) of the raw data  102  included in the current batch, as well as a local peak (7) and a local valley (2) (the data records highlighted in table  2102 ). In some embodiments, the No Mode strategy may only include the maximum, minimum, and local peaks—omitting the local valleys—if only the peaks are of interest to the cloud-side applications. In yet other embodiments, the No Mod strategy may include the maximum, minimum, and local valleys, omitting the peaks. 
     As noted above, in addition to reducing the raw data  102  in accordance with the data reduction strategy selected by the modal analysis component  310 , the data reduction component  312  also defines an association between each item of the reduced data set  702  and the subset of the raw data  102  surrounding the data item.  FIG. 22  depicts a table  2202  representing an example set of raw data and a table  2204  depicting a reduced data set that has been generated by the data reduction component  312  based on a selected data reduction strategy. In addition to creating the reduced data set represented by table  2204 , the data reduction component  312  also defines associations or linkages between each data record of the reduced data set (table  2204 ) and its corresponding record in the raw data set (table  2202 ). To this end, the data reduction component  312  can generate and add a unique identifier (UID) to each item of raw data (the UID column in table  2202 ). The UID uniquely identifies its corresponding item of raw, such that the UID for a given item of raw data is unique to that data item. When an item of the raw data is selected for inclusion in the reduced data set, the UID for the selected data item is copied with the data item to the reduced data set and maintains its association with the data record. This creates a linkage between each data record in the reduced data set and its corresponding data record in the raw data set by virtue of the common UID between the two records. These linkages are represented by the lines in  FIG. 22  that connect each data record in table  2204  with its corresponding data record in table  2202 . 
       FIG. 23  is a diagram illustrating an example IIoT data pipeline architecture that includes at least one pipeline node system  302  as part of the pipeline backbone. As in the example architecture depicted in  FIG. 1 , an edge device  106  that interfaces the industrial devices on the plant floor with the data pipeline  114  collects raw data  102   a  from the industrial devices and feeds the data into the pipeline  114  via node system  302 . Either the node system  302  or the edge device  106  can pre-process the raw data  102   a —e.g., by removing outlier data or non-numerical data, sorting the data  102   a  according to time stamps, etc.—to yield processed raw data  102   b . The node system  302  or the edge device  106  can also select and apply a suitable data reduction algorithm based on analysis of the raw data  102   b  to yield a reduced data set  702 , as discussed in previous examples. The data reduction component  312  of either the pipeline node system  302  or the edge device  106  also defines associative links between each item of the reduced data set  702  and its corresponding data item in the raw data  102   b . Both the reduced data asset  702  and the processed raw data  102   b  are then conveyed by the node system  302  (by the data output component  306 ) to the final storage destination for the data, together with the defined associations between the reduced data set  702  and the raw data  102   b . In this example, the data is streamed to cloud storage  110  for storage and consumption by cloud-based applications  112  (e.g., visualization, reporting, or analytics applications). 
     In the case of visualization applications that provide graphical or alphanumeric views of the collected data, the defined associations or links between the reduced data set  702  and the raw data  102   b  can allow users to switch between a high-level overview populated by the reduced data set  702  and a more detailed view populated by the raw data  102 .  FIG. 24  is a screenshot of an example graph  2402  that can be rendered by a visualization application based on the reduced data set  702 . This graph conveys time-series values of a data tag as a line on the graph  2402 , which is drawn based on the values of the reduced data set  702 . Since the graph  2402  is plotted based on the reduced data set  702 , which omits values from deemed less significant, only the most significant values are plotted. This results in a plot that is free of noise that would otherwise be present if all time-series values from the raw data  102  were plotted. 
     Since the raw data  102  is also stored on the cloud platform together with defined associations between the reduced data set  702  and the raw data  102 , the user can also drill down to a more detailed view for any point in time represented in the graph  2402 .  FIG. 25  is an example graph  2502  that can be invoked by the user. Graph  2502  plots the raw data  102  corresponding to the reduced data  702 , thereby allowing the user to view the values of the data tags at a more granular level (e.g., at a higher time density). The raw data  102  can be visualized in any suitable format depending on the type of visualization application that renders the data (e.g., as a bar chart, a list of alphanumeric values, etc.). In an example scenario, the user can invoke the graph  2502  by selecting a point on the graph  2402 , which invokes a version of the data that includes the selected data point and the records of the raw data  102  in temporal proximity to the selected data point. 
     The IIoT data reduction approach implemented by embodiments of the node system  302  described herein can intelligently filter sets of raw industrial data in a manner that maintains the most critical data, as determined based on analysis of the data&#39;s probability distribution. In contrast to approaches that apply the same data reduction algorithm to all data batches, the approach described herein can maintain data consistency and accuracy of the reduced data set while also retaining associations with the corresponding raw data. 
       FIGS. 26 a -26 e    illustrate a methodology in accordance with one or more embodiments of the subject application. While, for purposes of simplicity of explanation, the methodology shown herein is shown and described as a series of acts, it is to be understood and appreciated that the subject innovation is not limited by the order of acts, as some acts may, in accordance therewith, occur in a different order and/or concurrently with other acts from that shown and described herein. For example, those skilled in the art will understand and appreciate that a methodology could alternatively be represented as a series of interrelated states or events, such as in a state diagram. Moreover, not all illustrated acts may be required to implement a methodology in accordance with the innovation. Furthermore, interaction diagram(s) may represent methodologies, or methods, in accordance with the subject disclosure when disparate entities enact disparate portions of the methodologies. Further yet, two or more of the disclosed example methods can be implemented in combination with each other, to accomplish one or more features or advantages described herein. 
     In some embodiments, the methodology depicted in  FIGS. 26 a -26 e    can be performed for each data transmission cycle of a reactive node of an IIoT data pipeline. 
       FIG. 26 a    illustrates a first part of an example methodology  2600   a  for generating a reduced data set from a raw data batch comprising industrial data collected from industrial devices on a plant floor. Initially, at  2602 , a data batch is received and queued at a node of an IIoT data pipeline (or an edge device that feeds industrial data into the data pipeline). The data batch comprises time-series values of a data tag generated by an industrial device in a plant facility. At  2604 , the raw industrial data is pre-processed; e.g., by removing outlier data, removing non-numerical data, organizing or sorting the data according to time stamp, etc. 
     At  2606 , a determination is made as to whether the size of the data batch is less than or equal to five data records. If the size of the data batch is less than or equal to five (YES at step  2606 ), the methodology proceeds to the second part  2600   b  illustrated in  FIG. 26 b   . At  2612 , a determination is made as to whether the data batch comprises only one data record. If the batch contains only one data record (YES at step  2612 ), the methodology proceeds to step  2614 , where the value of the single data item, its associated time stamp, and its tag identifier are sent to the next node in the pipeline or to a final destination (e.g., cloud platform storage). Alternatively, if the data batch contains more than one data record (NO at step  2612 ), the methodology proceeds to step  2616 , where a determination is made as to whether the data batch contains 2-3 data items. If the data batch contains 2-3 data records (YES at step  2616 ), the methodology proceeds to step  2618 , where the maximum value and the minimum value contained in the data batch, as well as their associated time stamps and a tag identifier, are sent to the next node or the final destination. Alternatively, if the size of the data batch is greater than three (that is, the batch contains 4 or 5 data records) (NO at step  2616 ), the methodology proceeds to step  2620 , where a probability distribution of the values contained in the data batch is generated. Then, at  2622 , the node sends, as a reduced data set, the maximum, minimum, and mode values (if mode values exist) contained in the data batch (where the mode value is determined based on the probability distribution generated at step  2620 ), as well as the time stamps associated with those values and a tag identifier. 
     Returning to the first part of the methodology  2600   a  illustrated in  FIG. 26 a   , if the size of the data batch is greater than five data records (NO at step  2606 ), the methodology proceeds to step  2608 , where a determination is made as to whether the data values are equal across the entire data batch (e.g., the maximum value is equal to the minimum values). If the value of the data tag is constant across the entire data batch (YES at step  2608 ), the methodology proceeds to step  2610 , where the node sends, as a reduced data set, a data record selected from a midpoint or approximate midpoint in time of the data batch, as well as the time stamp associated with this data record and a tag identifier to the next node in the pipeline or to the final destination (the No Change data reduction strategy). Alternatively, if the data values are not equal across the entire batch (NO at step  2608 ), the methodology proceeds to the third part  2600   c  illustrated in  FIG. 26   c.    
     At  2624 , a weighted moving average of the value of the data tag across the data batch is calculated (e.g., using equation (3) or a variation thereof). At  2626 , a determination is made as to whether the difference between each value in the data batch and the weighted moving average calculated at step  2624  is less than a defined delta. If so (YES at step  2626 ), the methodology proceeds to step  2628 , where the node sends, as a reduced data set, a data record comprising the median value of the data tag, a time stamp selected from a midpoint or approximate midpoint of the data batch, and a tag identifier to the next node or the final destination (the Small Change data reduction strategy). Alternatively, if the difference between any of the values in the data batch and the weighted moving average is greater than the defined delta value (NO at step  2626 ), the methodology proceeds to the fourth part  2600   d  illustrated in  FIG. 26   d.    
     At  2630 , a probability distribution of the values contained in the data batch is generated. At  2632 , any modes in the probability distribution are identified. At  2634 , a determination is made as to whether the probability distribution is unimodal. If the probability distribution is unimodal (YES at step  2634 ), the methodology proceeds to step  2636 , where the node sends, as a reduced data batch, data records from the raw data batch corresponding to the maximum value, the minimum value, and the single mode value, as well as the time stamps corresponding to these data items and a tag identifier, to the next node or the final destination (the Unimodal data reduction strategy). 
     Alternatively, if the probability distribution is not unimodal (NO at step  2634 ), the methodology proceeds to step  2638 , where a determination is made as to whether the probability distribution is multimodal. If the probability distribution is multimodal (YES at step  2638 ), the methodology proceeds to step  2640 , where the node sends, as a reduced data set, the data records from the raw data set corresponding to the maximum value, the minimum value, and each of the multiple mode values, as well as time stamps corresponding to each of these data records and a tag identifier, to the next node or the final destination (the Multimodal data reduction strategy). 
     Alternatively, if the probability distribution is not multimodal (NO at step  2638 ), the methodology proceeds to the fifth part  2600   e  illustrated in  FIG. 26 e   . At  2642 , the node sends, as a reduced data set, data records from the raw data set corresponding to the maximum value, the minimum value, any local peak values, and any local valley values, as well as the time stamps corresponding to these data records and a tag identifier, to the next node or the final destination. 
     Embodiments, systems, and components described herein, as well as control systems and automation environments in which various aspects set forth in the subject specification can be carried out, can include computer or network components such as servers, clients, programmable logic controllers (PLCs), automation controllers, communications modules, mobile computers, on-board computers for mobile vehicles, wireless components, control components and so forth which are capable of interacting across a network. Computers and servers include one or more processors—electronic integrated circuits that perform logic operations employing electric signals—configured to execute instructions stored in media such as random access memory (RAM), read only memory (ROM), a hard drives, as well as removable memory devices, which can include memory sticks, memory cards, flash drives, external hard drives, and so on. 
     Similarly, the term PLC or automation controller as used herein can include functionality that can be shared across multiple components, systems, and/or networks. As an example, one or more PLCs or automation controllers can communicate and cooperate with various network devices across the network. This can include substantially any type of control, communications module, computer, Input/Output (I/O) device, sensor, actuator, and human machine interface (HMI) that communicate via the network, which includes control, automation, and/or public networks. The PLC or automation controller can also communicate to and control various other devices such as standard or safety-rated I/O modules including analog, digital, programmed/intelligent I/O modules, other programmable controllers, communications modules, sensors, actuators, output devices, and the like. 
     The network can include public networks such as the internet, intranets, and automation networks such as control and information protocol (CIP) networks including DeviceNet, ControlNet, safety networks, and Ethernet/IP. Other networks include Ethernet, DH/DH+, Remote I/O, Fieldbus, Modbus, Profibus, CAN, wireless networks, serial protocols, and so forth. In addition, the network devices can include various possibilities (hardware and/or software components). These include components such as switches with virtual local area network (VLAN) capability, LANs, WANs, proxies, gateways, routers, firewalls, virtual private network (VPN) devices, servers, clients, computers, configuration tools, monitoring tools, and/or other devices. 
     In order to provide a context for the various aspects of the disclosed subject matter,  FIGS. 27 and 28  as well as the following discussion are intended to provide a brief, general description of a suitable environment in which the various aspects of the disclosed subject matter may be implemented. While the embodiments have been described above in the general context of computer-executable instructions that can run on one or more computers, those skilled in the art will recognize that the embodiments can be also implemented in combination with other program modules and/or as a combination of hardware and software. 
     Generally, program modules include routines, programs, components, data structures, etc., that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the inventive methods can be practiced with other computer system configurations, including single-processor or multiprocessor computer systems, minicomputers, mainframe computers, Internet of Things (IoT) devices, distributed computing systems, as well as personal computers, hand-held computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which can be operatively coupled to one or more associated devices. 
     The illustrated embodiments herein can be also practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices. 
     Computing devices typically include a variety of media, which can include computer-readable storage media, machine-readable storage media, and/or communications media, which two terms are used herein differently from one another as follows. Computer-readable storage media or machine-readable storage media can be any available storage media that can be accessed by the computer and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer-readable storage media or machine-readable storage media can be implemented in connection with any method or technology for storage of information such as computer-readable or machine-readable instructions, program modules, structured data or unstructured data. 
     Computer-readable storage media can include, but are not limited to, random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disk read only memory (CD-ROM), digital versatile disk (DVD), Blu-ray disc (BD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, solid state drives or other solid state storage devices, or other tangible and/or non-transitory media which can be used to store desired information. In this regard, the terms “tangible” or “non-transitory” herein as applied to storage, memory or computer-readable media, are to be understood to exclude only propagating transitory signals per se as modifiers and do not relinquish rights to all standard storage, memory or computer-readable media that are not only propagating transitory signals per se. 
     Computer-readable storage media can be accessed by one or more local or remote computing devices, e.g., via access requests, queries or other data retrieval protocols, for a variety of operations with respect to the information stored by the medium. 
     Communications media typically embody computer-readable instructions, data structures, program modules or other structured or unstructured data in a data signal such as a modulated data signal, e.g., a carrier wave or other transport mechanism, and includes any information delivery or transport media. The term “modulated data signal” or signals refers to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in one or more signals. By way of example, and not limitation, communication media include wired media, such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. 
     With reference again to  FIG. 27  the example environment  2700  for implementing various embodiments of the aspects described herein includes a computer  2702 , the computer  2702  including a processing unit  2704 , a system memory  2706  and a system bus  2708 . The system bus  2708  couples system components including, but not limited to, the system memory  2706  to the processing unit  2704 . The processing unit  2704  can be any of various commercially available processors. Dual microprocessors and other multi-processor architectures can also be employed as the processing unit  2704 . 
     The system bus  2708  can be any of several types of bus structure that can further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory  2706  includes ROM  2710  and RAM  2712 . A basic input/output system (BIOS) can be stored in a non-volatile memory such as ROM, erasable programmable read only memory (EPROM), EEPROM, which BIOS contains the basic routines that help to transfer information between elements within the computer  2702 , such as during startup. The RAM  2712  can also include a high-speed RAM such as static RAM for caching data. 
     The computer  2702  further includes an internal hard disk drive (HDD)  2714  (e.g., EIDE, SATA), one or more external storage devices  2716  (e.g., a magnetic floppy disk drive (FDD)  2716 , a memory stick or flash drive reader, a memory card reader, etc.) and an optical disk drive  2720  (e.g., which can read or write from a CD-ROM disc, a DVD, a BD, etc.). While the internal HDD  2714  is illustrated as located within the computer  2702 , the internal HDD  2714  can also be configured for external use in a suitable chassis (not shown). Additionally, while not shown in environment  2700 , a solid state drive (SSD) could be used in addition to, or in place of, an HDD  2714 . The HDD  2714 , external storage device(s)  2716  and optical disk drive  2720  can be connected to the system bus  2708  by an HDD interface  2724 , an external storage interface  2726  and an optical drive interface  2728 , respectively. The interface  2724  for external drive implementations can include at least one or both of Universal Serial Bus (USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394 interface technologies. Other external drive connection technologies are within contemplation of the embodiments described herein. 
     The drives and their associated computer-readable storage media provide nonvolatile storage of data, data structures, computer-executable instructions, and so forth. For the computer  2702 , the drives and storage media accommodate the storage of any data in a suitable digital format. Although the description of computer-readable storage media above refers to respective types of storage devices, it should be appreciated by those skilled in the art that other types of storage media which are readable by a computer, whether presently existing or developed in the future, could also be used in the example operating environment, and further, that any such storage media can contain computer-executable instructions for performing the methods described herein. 
     A number of program modules can be stored in the drives and RAM  2712 , including an operating system  2730 , one or more application programs  2732 , other program modules  2734  and program data  2736 . All or portions of the operating system, applications, modules, and/or data can also be cached in the RAM  2712 . The systems and methods described herein can be implemented utilizing various commercially available operating systems or combinations of operating systems. 
     Computer  2702  can optionally comprise emulation technologies. For example, a hypervisor (not shown) or other intermediary can emulate a hardware environment for operating system  2730 , and the emulated hardware can optionally be different from the hardware illustrated in  FIG. 27 . In such an embodiment, operating system  2730  can comprise one virtual machine (VM) of multiple VMs hosted at computer  2702 . Furthermore, operating system  2730  can provide runtime environments, such as the Java runtime environment or the .NET framework, for application programs  2732 . Runtime environments are consistent execution environments that allow application programs  2732  to run on any operating system that includes the runtime environment. Similarly, operating system  2730  can support containers, and application programs  2732  can be in the form of containers, which are lightweight, standalone, executable packages of software that include, e.g., code, runtime, system tools, system libraries and settings for an application. 
     Further, computer  2702  can be enable with a security module, such as a trusted processing module (TPM). For instance with a TPM, boot components hash next in time boot components, and wait for a match of results to secured values, before loading a next boot component. This process can take place at any layer in the code execution stack of computer  2702 , e.g., applied at the application execution level or at the operating system (OS) kernel level, thereby enabling security at any level of code execution. 
     A user can enter commands and information into the computer  2702  through one or more wired/wireless input devices, e.g., a keyboard  2738 , a touch screen  2740 , and a pointing device, such as a mouse  2742 . Other input devices (not shown) can include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control, or other remote control, a joystick, a virtual reality controller and/or virtual reality headset, a game pad, a stylus pen, an image input device, e.g., camera(s), a gesture sensor input device, a vision movement sensor input device, an emotion or facial detection device, a biometric input device, e.g., fingerprint or iris scanner, or the like. These and other input devices are often connected to the processing unit  2704  through an input device interface  2744  that can be coupled to the system bus  2708 , but can be connected by other interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, a BLUETOOTH® interface, etc. 
     A monitor  2744  or other type of display device can be also connected to the system bus  2708  via an interface, such as a video adapter  2746 . In addition to the monitor  2744 , a computer typically includes other peripheral output devices (not shown), such as speakers, printers, etc. 
     The computer  2702  can operate in a networked environment using logical connections via wired and/or wireless communications to one or more remote computers, such as a remote computer(s)  2748 . The remote computer(s)  2748  can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment appliance, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer  2702 , although, for purposes of brevity, only a memory/storage device  2750  is illustrated. The logical connections depicted include wired/wireless connectivity to a local area network (LAN)  2752  and/or larger networks, e.g., a wide area network (WAN)  2754 . Such LAN and WAN networking environments are commonplace in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can connect to a global communications network, e.g., the Internet. 
     When used in a LAN networking environment, the computer  2702  can be connected to the local network  2752  through a wired and/or wireless communication network interface or adapter  2756 . The adapter  2756  can facilitate wired or wireless communication to the LAN  2752 , which can also include a wireless access point (AP) disposed thereon for communicating with the adapter  2756  in a wireless mode. 
     When used in a WAN networking environment, the computer  2702  can include a modem  2758  or can be connected to a communications server on the WAN  2754  via other means for establishing communications over the WAN  2754 , such as by way of the Internet. The modem  2758 , which can be internal or external and a wired or wireless device, can be connected to the system bus  2708  via the input device interface  2742 . In a networked environment, program modules depicted relative to the computer  2702  or portions thereof, can be stored in the remote memory/storage device  2750 . It will be appreciated that the network connections shown are example and other means of establishing a communications link between the computers can be used. 
     When used in either a LAN or WAN networking environment, the computer  2702  can access cloud storage systems or other network-based storage systems in addition to, or in place of, external storage devices  2716  as described above. Generally, a connection between the computer  2702  and a cloud storage system can be established over a LAN  2752  or WAN  2754  e.g., by the adapter  2756  or modem  2758 , respectively. Upon connecting the computer  2702  to an associated cloud storage system, the external storage interface  2726  can, with the aid of the adapter  2756  and/or modem  2758 , manage storage provided by the cloud storage system as it would other types of external storage. For instance, the external storage interface  2726  can be configured to provide access to cloud storage sources as if those sources were physically connected to the computer  2702 . 
     The computer  2702  can be operable to communicate with any wireless devices or entities operatively disposed in wireless communication, e.g., a printer, scanner, desktop and/or portable computer, portable data assistant, communications satellite, any piece of equipment or location associated with a wirelessly detectable tag (e.g., a kiosk, news stand, store shelf, etc.), and telephone. This can include Wireless Fidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, the communication can be a predefined structure as with a conventional network or simply an ad hoc communication between at least two devices. 
       FIG. 28  is a schematic block diagram of a sample computing environment  2800  with which the disclosed subject matter can interact. The sample computing environment  2800  includes one or more client(s)  2802 . The client(s)  2802  can be hardware and/or software (e.g., threads, processes, computing devices). The sample computing environment  2800  also includes one or more server(s)  2804 . The server(s)  2804  can also be hardware and/or software (e.g., threads, processes, computing devices). The servers  2804  can house threads to perform transformations by employing one or more embodiments as described herein, for example. One possible communication between a client  2802  and servers  2804  can be in the form of a data packet adapted to be transmitted between two or more computer processes. The sample computing environment  2800  includes a communication framework  2806  that can be employed to facilitate communications between the client(s)  2802  and the server(s)  2804 . The client(s)  2802  are operably connected to one or more client data store(s)  2808  that can be employed to store information local to the client(s)  2802 . Similarly, the server(s)  2804  are operably connected to one or more server data store(s)  2810  that can be employed to store information local to the servers  2804 . 
     What has been described above includes examples of the subject innovation. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the disclosed subject matter, but one of ordinary skill in the art may recognize that many further combinations and permutations of the subject innovation are possible. Accordingly, the disclosed subject matter is intended to embrace all such alterations, modifications, and variations that fall within the spirit and scope of the appended claims. 
     In particular and in regard to the various functions performed by the above described components, devices, circuits, systems and the like, the terms (including a reference to a “means”) used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (e.g., a functional equivalent), even though not structurally equivalent to the disclosed structure, which performs the function in the herein illustrated exemplary aspects of the disclosed subject matter. In this regard, it will also be recognized that the disclosed subject matter includes a system as well as a computer-readable medium having computer-executable instructions for performing the acts and/or events of the various methods of the disclosed subject matter. 
     In addition, while a particular feature of the disclosed subject matter may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application. Furthermore, to the extent that the terms “includes,” and “including” and variants thereof are used in either the detailed description or the claims, these terms are intended to be inclusive in a manner similar to the term “comprising.” 
     In this application, the word “exemplary” is used to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather, use of the word exemplary is intended to present concepts in a concrete fashion. 
     Various aspects or features described herein may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. The term “article of manufacture” as used herein is intended to encompass a computer program accessible from any computer-readable device, carrier, or media. For example, computer readable media can include but are not limited to magnetic storage devices (e.g., hard disk, floppy disk, magnetic strips . . . ), optical disks [e.g., compact disk (CD), digital versatile disk (DVD) . . . ], smart cards, and flash memory devices (e.g., card, stick, key drive . . . ).