Patent Publication Number: US-2023161645-A1

Title: Classification of events by pattern recognition in multivariate time series data

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Patent Application No. 63/282,917, titled “CLASSIFICATION OF EVENTS BY PATTERN RECOGNITION IN MULTIVARIATE TIME SERIES DATA,” and filed on Nov. 24, 2021, the entirety of which is hereby incorporated by reference. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates generally to real-time asset analytics, and more particularly to classification of events by pattern recognition in multivariate time series data associated with one or more assets 
     BACKGROUND 
     Traditionally, data analytics and/or digital transformation of data related to an industrial environment generally involves human interaction associated with a specialized worker (e.g., via engineering analysis of the data). Furthermore, a limited amount of time is traditionally spent on modeling of data related to an industrial environment to, for example, provide insights related to the data. As such, computing resources related to data analytics and/or digital transformation of data related to an industrial environment are traditionally employed in an inefficient manner. 
     SUMMARY 
     The details of some embodiments of the subject matter described in this specification are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims. 
     In an embodiment, a system comprises one or more processors, a memory, and one or more programs stored in the memory. The one or more programs comprise instructions configured to receive a request to classify events associated with one or more assets. In one or more embodiments, the request comprises an asset descriptor describing the one or more assets. In one or more embodiments, in response to the request, the one or more programs further comprise instructions configured to obtain, based on the asset descriptor, aggregated multivariate data associated with the one or more assets. In one or more embodiments, in response to the request, the one or more programs further comprise instructions to determine a data signature for at least a portion of the aggregated multivariate data that corresponds to sensor output of the one or more assets. In one or more embodiments, in response to the request, the one or more programs further comprise instructions to determine, based on respective defined data signatures for respective defined events associated with a defined event attribute, a respective label for one or more events associated with the data signature. In one or more embodiments, in response to the request, the one or more programs further comprise instructions to provide a dashboard visualization to an electronic interface of a computing device, the dashboard visualization comprising data associated with the one or more events. 
     In another embodiment, a method comprises, at a device with one or more processors and a memory, receiving a request to classify events associated with one or more assets. In one or more embodiments, the request comprises an asset descriptor describing the one or more assets. In one or more embodiments, the method further comprises, at the device and in response to the request, obtaining, based on the asset descriptor, aggregated multivariate data associated with the one or more assets. In one or more embodiments, the method further comprises, at the device and in response to the request, determining a data signature for at least a portion of the aggregated multivariate data that corresponds to sensor output of the one or more assets. In one or more embodiments, the method further comprises, at the device and in response to the request, determining, based on respective defined data signatures for respective defined events associated with a defined event attribute, a respective label for one or more events associated with the data signature. In one or more embodiments, the method further comprises, at the device and in response to the request, providing a dashboard visualization to an electronic interface of a computing device, the dashboard visualization comprising data associated with the one or more events. 
     In yet another embodiment, a computer program product comprises at least one computer-readable storage medium having program instructions embodied thereon. The program instructions are executable by a processor to cause the processor to receive a request to classify events associated with one or more assets. In one or more embodiments, the request comprises an asset descriptor describing the one or more assets. In one or more embodiments, in response to the request, the program instructions further cause the processor to obtain, based on the asset descriptor, aggregated multivariate data associated with the one or more assets. In one or more embodiments, in response to the request, the program instructions further cause the processor to determine a data signature for at least a portion of the aggregated multivariate data that corresponds to sensor output of the one or more assets. In one or more embodiments, in response to the request, the program instructions further cause the processor to determine, based on respective defined data signatures for respective defined events associated with a defined event attribute, a respective label for one or more events associated with the data signature. In one or more embodiments, in response to the request, the program instructions further cause the processor to provide a dashboard visualization to an electronic interface of a computing device, the dashboard visualization comprising data associated with the one or more events. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The description of the illustrative embodiments can be read in conjunction with the accompanying figures. It will be appreciated that for simplicity and clarity of illustration, elements illustrated in the figures have not necessarily been drawn to scale. For example, the dimensions of some of the elements are exaggerated relative to other elements. Embodiments incorporating teachings of the present disclosure are shown and described with respect to the figures presented herein, in which: 
         FIG.  1    illustrates an exemplary networked computing system environment, in accordance with one or more embodiments described herein; 
         FIG.  2    illustrates a schematic block diagram of a framework of an IoT platform of the networked computing system, in accordance with one or more embodiments described herein; 
         FIG.  3    illustrates a system that provides an exemplary environment, in accordance with one or more embodiments described herein; 
         FIG.  4    illustrates an exemplary event classification component, in accordance with one or more embodiments described herein; 
         FIG.  5    illustrates another system that provides an exemplary environment, in accordance with one or more embodiments described herein; 
         FIG.  6    illustrates an exemplary computing device, in accordance with one or more embodiments described herein; 
         FIG.  7    illustrates an exemplary electronic interface, in accordance with one or more embodiments described herein; 
         FIG.  8    illustrates another exemplary electronic interface, in accordance with one or more embodiments described herein; 
         FIG.  9    illustrates another exemplary electronic interface, in accordance with one or more embodiments described herein; 
         FIG.  10    illustrates another exemplary electronic interface, in accordance with one or more embodiments described herein; 
         FIG.  11    illustrates a flow diagram for classification of events using pattern recognition in multivariate data, in accordance with one or more embodiments described herein; and 
         FIG.  12    illustrates a functional block diagram of a computer that may be configured to execute techniques described in accordance with one or more embodiments described herein. 
     
    
    
     DETAILED DESCRIPTION 
     Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the various described embodiments. However, it will be apparent to one of ordinary skill in the art that the various described embodiments may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments. The term “or” is used herein in both the alternative and conjunctive sense, unless otherwise indicated. The terms “illustrative,” “example,” and “exemplary” are used to be examples with no indication of quality level. Like numbers refer to like elements throughout. 
     The phrases “in an embodiment,” “in one embodiment,” “according to one embodiment,” and the like generally mean that the particular feature, structure, or characteristic following the phrase can be included in at least one embodiment of the present disclosure, and can be included in more than one embodiment of the present disclosure (importantly, such phrases do not necessarily refer to the same embodiment). 
     The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any implementation described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other implementations. 
     If the specification states a component or feature “can,” “may,” “could,” “should,” “would,” “preferably,” “possibly,” “typically,” “optionally,” “for example,” “often,” or “might” (or other such language) be included or have a characteristic, that particular component or feature is not required to be included or to have the characteristic. Such component or feature can be optionally included in some embodiments, or it can be excluded. 
     In general, the present disclosure provides for an “Internet-of-Things” or “IoT” platform for enterprise performance management that uses real-time accurate models and visual analytics to deliver intelligent actionable recommendations for sustained peak performance of an enterprise or organization. The IoT platform is an extensible platform that is portable for deployment in any cloud or data center environment for providing an enterprise-wide, top to bottom view, displaying the status of processes, assets, people, and safety. Further, the IoT platform of the present disclosure supports end-to-end capability to execute digital twins against process data and to translate the output into actionable insights, as detailed in the following description. 
     Traditionally, data analytics and/or digital transformation of data related to an industrial environment generally involves human interaction associated with a specialized worker (e.g., via engineering analysis of the data). Furthermore, a limited amount of time is traditionally spent on modeling of data related to an industrial environment to, for example, provide insights related to the data. As such, computing resources related to data analytics and/or digital transformation of data related to an industrial environment are traditionally employed in an inefficient manner. Moreover, it is generally difficult to provide data driven automatic labeling of certain events associated with an industrial environment such as, for example, rare events (e.g., large ratio of features to outcome) in multivariate data associated with the industrial environment. 
     Thus, to address these and/or other issues, classification of events using pattern recognition with respect to multivariate time series data is provided. In various embodiments, the multivariate time series data is associated with one or more assets and/or one or more processes (e.g., one or more assets and/or one or more processes within an industrial environment). In one or more embodiments, data driven automatic labeling of rare events in the multivariate data is provided using data preprocessing, data augmentation, training of machine learning classifiers, candidate data classification using machine learning classifiers, and/or variable trend prediction. 
     In one or more embodiments, a user interface is provided to label events in historical data (e.g., using a tabular-type user interface), display a list of event periods previously labeled under a particular event label type, display a user label for an event that is different than a predicted label provided by a machine learning classifier for the event, display one or more trends of variables during the event and/or before the event, enable a user to modify an identified event label, enable a user to initiate retraining of a machine learning classifier, and/or display other data associated with classification of events using pattern recognition. In one or more embodiments, the user interface provides a tool to select multiple variables (e.g., tags) that may influence an event, to mark and/or label time periods of interest (e.g., to label rare events), and/or to receive one or more notifications in response to performance criteria associated with one or more assets and/or one or more processes. 
     In one or more embodiments, classification of events using pattern recognition employs user labels periods of historic data provided via the user interface. Additionally, in one or more embodiments, classification of events using pattern recognition includes training of one or more machine learning classifiers to recognize one or more signatures of data in each period labeled via the user interface, providing real-time streaming data to the classification system during run time, evaluating the real-time streaming data using one or more trained machine learning classifiers to identify presence of one or more predetermined data signatures, generating one or more notification for the user interface in response to a predetermined data signature being identified in the real-time streaming data, displaying one or more metrics of the data signature match via the user interface, configuring the user interface to provide a user an ability to accept the notification or mark the notification as incorrect, configuring the user interface to provide a user an ability to specify a correct label for the data signature match, and/or retraining of one or more machine learning classifiers based on the correct label for the data signature match. 
     In one or more embodiments, test data (e.g., candidate data) used in an anomaly detection process may be employed as training data for event classification. For example, in one or more embodiments in response to detection of an anomaly, the user interface is configured to allow a user to label the anomaly and/or to invoke an event classification process. In one or more embodiments a data preprocessing module is configured to read training data using one or more data pipelines, clean data (e.g., remove undefined data, remove redundant columns associated with constant data values, etc.), perform feature engineering (e.g., group data into batches of fixed time intervals and/or extract statistical features from respective groups), scale and/or normalize data (e.g., using a min-max scaling technique, etc.), generate data interaction columns (e.g., generate additional columns by multiplying existing columns), remove high correlated feature columns, and/or perform one or more types of preprocessing. 
     In one or more embodiments, data augmentation is performed to modify one or more data records (e.g., by over sampling, under sampling, and/or generating artificial examples). In one or more embodiments, data augmentation is performed to provide suitable data to train the rare events. In one or more embodiments, one or more data augmentation techniques are selected and/or applied to augment data based on data associated with a predicted rare event and/or rule weighting. In one or more embodiments, a machine learning classifier is trained to detect whether a specific unique event is present in a dataset. In one or more embodiments, training of a machine learning classifier is triggered in response to labeling of an event (e.g., by a user). In one or more embodiments, in response to multiple event labels being added, respective classifier instances are created for the respective event labels. In one or more embodiments, one or more training techniques are selected and/or applied to augment data based on data associated with a predicted rare event and/or rule weighting. In one or more embodiments, a training process includes acquiring one or more hyperparameters for a machine learning classifier, training a machine learning classifier to detect presence or absence of a particular event, storing a machine learning classifier for respective events, calculating a training score for respective machine learning classifiers, and/or displaying training score for respective machine learning classifiers via the user interface. In one or more embodiments, time series data during an event is summarized for each signal and reported via the user interface as a “Step Increase”, a “Step Decrease”, a “Slope Increase”, a “Slope Decrease”, or an “Oscillation”. In one or more embodiments, variable trend summary for an event is provided by calculating slope, intercept, and/or degree of oscillation for a signal before the event and/or during the event. 
     In various embodiments, a dashboard visualization across various user identities is provided via a templated dashboard model using, for example, an extensible object model. In various embodiments, metrics associated with a fourth asset hierarchy level (e.g., an asset level) includes events or exception that are related to a target goal. In one or more embodiments, the dashboard visualization allows a user to see how one or more assets are performing against one or more metrics (e.g., one or more KPIs). In one or more embodiments, the dashboard visualization allows a user to identify what next steps with respect to assets will provide an optimal return on investment for the action (e.g., repair device #1 vs. device #2) depending on the metrics (e.g., fixing device #1 will save X % energy, whereas repairing device #2 will save $Y). In one or more embodiments, the dashboard visualization allows a user to view individual assets through the dashboard (e.g., boiler #1 is operating at 90% efficiency, or will fail in X weeks, Y days, Z hours unless action is taken; and repairing the boiler #1 within a first interval of time will save $X, whereas repairing within a second interval of time will save $Y). In one or more embodiments, the dashboard visualization allows a user to change individual settings for an asset remotely. In one or more embodiments, the dashboard visualization notifies a user that changing settings for an asset from X to Y (e.g., in response to prediction of an event) will save X % energy or $Y. 
     As such, by employing one or more techniques disclosed herein, asset performance and/or process performance is optimized. Additionally, by employing one or more techniques disclosed herein, accuracy of a machine learning classifier is improved. Moreover, by employing one or more techniques disclosed herein, improved insights for opportunity and/or performance insights for assets and/or processes is provided to a user via improved visual indicators associated with a graphical user interface. For instance, by employing one or more techniques disclosed herein, additional and/or improved asset insights as compared to capabilities of conventional techniques can be achieved across a data set. Additionally, performance of a processing system associated with data analytics is improved by employing one or more techniques disclosed herein. For example, a number of computing resources, a number of a storage requirements, and/or number of errors associated with data analytics is reduced by employing one or more techniques disclosed herein. 
       FIG.  1    illustrates an exemplary networked computing system environment  100 , according to the present disclosure. As shown in  FIG.  1   , networked computing system environment  100  is organized into a plurality of layers including a cloud  105  (e.g., a cloud layer  105 ), a network  110  (e.g., a network layer  110 ), and an edge  115  (e.g., an edge layer  115 ). As detailed further below, components of the edge  115  are in communication with components of the cloud  105  via network  110 . 
     In various embodiments, network  110  is any suitable network or combination of networks and supports any appropriate protocol suitable for communication of data to and from components of the cloud  105  and between various other components in the networked computing system environment  100  (e.g., components of the edge  115 ). According to various embodiments, network  110  includes a public network (e.g., the Internet), a private network (e.g., a network within an organization), or a combination of public and/or private networks. According to various embodiments, network  110  is configured to provide communication between various components depicted in  FIG.  1   . According to various embodiments, network  110  comprises one or more networks that connect devices and/or components in the network layout to allow communication between the devices and/or components. For example, in one or more embodiments, the network  110  is implemented as the Internet, a wireless network, a wired network (e.g., Ethernet), a local area network (LAN), a Wide Area Network (WANs), Bluetooth, Near Field Communication (NFC), or any other type of network that provides communications between one or more components of the network layout. In some embodiments, network  110  is implemented using cellular networks, satellite, licensed radio, or a combination of cellular, satellite, licensed radio, and/or unlicensed radio networks. 
     Components of the cloud  105  include one or more computer systems  120  that form a so-called “Internet-of-Things” or “IoT” platform  125 . It should be appreciated that “IoT platform” is an optional term describing a platform connecting any type of Internet-connected device, and should not be construed as limiting on the types of computing systems useable within IoT platform  125 . In particular, in various embodiments, computer systems  120  includes any type or quantity of one or more processors and one or more data storage devices comprising memory for storing and executing applications or software modules of networked computing system environment  100 . In one embodiment, the processors and data storage devices are embodied in server-class hardware, such as enterprise-level servers. For example, in an embodiment, the processors and data storage devices comprise any type or combination of application servers, communication servers, web servers, super-computing servers, database servers, file servers, mail servers, proxy servers, and/virtual servers. Further, the one or more processors are configured to access the memory and execute processor-readable instructions, which when executed by the processors configures the processors to perform a plurality of functions of the networked computing system environment  100 . 
     Computer systems  120  further include one or more software components of the IoT platform  125 . For example, in one or more embodiments, the software components of computer systems  120  include one or more software modules to communicate with user devices and/or other computing devices through network  110 . For example, in one or more embodiments, the software components include one or more modules  141 , models  142 , engines  143 , databases  144 , services  145 , and/or applications  146 , which may be stored in/by the computer systems  120  (e.g., stored on the memory), as detailed with respect to  FIG.  2    below. According to various embodiments, the one or more processors are configured to utilize the one or more modules  141 , models  142 , engines  143 , databases  144 , services  145 , and/or applications  146  when performing various methods described in this disclosure. 
     Accordingly, in one or more embodiments, computer systems  120  execute a cloud computing platform (e.g., IoT platform  125 ) with scalable resources for computation and/or data storage, and may run one or more applications on the cloud computing platform to perform various computer-implemented methods described in this disclosure. In some embodiments, some of the modules  141 , models  142 , engines  143 , databases  144 , services  145 , and/or applications  146  are combined to form fewer modules, models, engines, databases, services, and/or applications. In some embodiments, some of the modules  141 , models  142 , engines  143 , databases  144 , services  145 , and/or applications  146  are separated into separate, more numerous modules, models, engines, databases, services, and/or applications. In some embodiments, some of the modules  141 , models  142 , engines  143 , databases  144 , services  145 , and/or applications  146  are removed while others are added. 
     The computer systems  120  are configured to receive data from other components (e.g., components of the edge  115 ) of networked computing system environment  100  via network  110 . Computer systems  120  are further configured to utilize the received data to produce a result. According to various embodiments, information indicating the result is transmitted to users via user computing devices over network  110 . In some embodiments, the computer systems  120  is a server system that provides one or more services including providing the information indicating the received data and/or the result(s) to the users. According to various embodiments, computer systems  120  are part of an entity which include any type of company, organization, or institution that implements one or more IoT services. In some examples, the entity is an IoT platform provider. 
     Components of the edge  115  include one or more enterprises  160   a - 160   n  each including one or more edge devices  161   a - 161   n  and one or more edge gateways  162   a - 162   n . For example, a first enterprise  160   a  includes first edge devices  161   a  and first edge gateways  162   a , a second enterprise  160   b  includes second edge devices  161   b  and second edge gateways  162   b , and an nth enterprise  160   n  includes nth edge devices  161   n  and nth edge gateways  162   n . As used herein, enterprises  160   a - 160   n  represent any type of entity, facility, or vehicle, such as, for example, companies, divisions, buildings, manufacturing plants, warehouses, real estate facilities, laboratories, aircraft, spacecraft, automobiles, ships, boats, military vehicles, oil and gas facilities, or any other type of entity, facility, and/or entity that includes any number of local devices. 
     According to various embodiments, the edge devices  161   a - 161   n  represent any of a variety of different types of devices that may be found within the enterprises  160   a - 160   n . Edge devices  161   a - 161   n  are any type of device configured to access network  110 , or be accessed by other devices through network  110 , such as via an edge gateway  162   a - 162   n . According to various embodiments, edge devices  161   a - 161   n  are “IoT devices” which include any type of network-connected (e.g., Internet-connected) device. For example, in one or more embodiments, the edge devices  161   a - 161   n  include assets, sensors, actuators, processors, computers, valves, pumps, ducts, vehicle components, cameras, displays, doors, windows, security components, boilers, chillers, pumps, HVAC components, factory equipment, and/or any other devices that are connected to the network  110  for collecting, sending, and/or receiving information. Each edge device  161   a - 161   n  includes, or is otherwise in communication with, one or more controllers for selectively controlling a respective edge device  161   a - 161   n  and/or for sending/receiving information between the edge devices  161   a - 161   n  and the cloud  105  via network  110 . With reference to  FIG.  2   , in one or more embodiments, the edge  115  include operational technology (OT) systems  163   a - 163   n  and information technology (IT) applications  164   a - 164   n  of each enterprise  161   a - 161   n . The OT systems  163   a - 163   n  include hardware and software for detecting and/or causing a change, through the direct monitoring and/or control of industrial equipment (e.g., edge devices  161   a - 161   n ), assets, processes, and/or events. The IT applications  164   a - 164   n  includes network, storage, and computing resources for the generation, management, storage, and delivery of data throughout and between organizations. 
     The edge gateways  162   a - 162   n  include devices for facilitating communication between the edge devices  161   a - 161   n  and the cloud  105  via network  110 . For example, the edge gateways  162   a - 162   n  include one or more communication interfaces for communicating with the edge devices  161   a - 161   n  and for communicating with the cloud  105  via network  110 . According to various embodiments, the communication interfaces of the edge gateways  162   a - 162   n  include one or more cellular radios, Bluetooth, WiFi, near-field communication radios, Ethernet, or other appropriate communication devices for transmitting and receiving information. According to various embodiments, multiple communication interfaces are included in each gateway  162   a - 162   n  for providing multiple forms of communication between the edge devices  161   a - 161   n , the gateways  162   a - 162   n , and the cloud  105  via network  110 . For example, in one or more embodiments, communication are achieved with the edge devices  161   a - 161   n  and/or the network  110  through wireless communication (e.g., WiFi, radio communication, etc.) and/or a wired data connection (e.g., a universal serial bus, an onboard diagnostic system, etc.) or other communication modes, such as a local area network (LAN), wide area network (WAN) such as the Internet, a telecommunications network, a data network, or any other type of network. 
     According to various embodiments, the edge gateways  162   a - 162   n  also include a processor and memory for storing and executing program instructions to facilitate data processing. For example, in one or more embodiments, the edge gateways  162   a - 162   n  are configured to receive data from the edge devices  161   a - 161   n  and process the data prior to sending the data to the cloud  105 . Accordingly, in one or more embodiments, the edge gateways  162   a - 162   n  include one or more software modules or components for providing data processing services and/or other services or methods of the present disclosure. With reference to  FIG.  2   , each edge gateway  162   a - 162   n  includes edge services  165   a - 165   n  and edge connectors  166   a - 166   n . According to various embodiments, the edge services  165   a - 165   n  include hardware and software components for processing the data from the edge devices  161   a - 161   n . According to various embodiments, the edge connectors  166   a - 166   n  include hardware and software components for facilitating communication between the edge gateway  162   a - 162   n  and the cloud  105  via network  110 , as detailed above. In some cases, any of edge devices  161   a - n , edge connectors  166   a - n , and edge gateways  162   a - n  have their functionality combined, omitted, or separated into any combination of devices. In other words, an edge device and its connector and gateway need not necessarily be discrete devices. 
       FIG.  2    illustrates a schematic block diagram of framework  200  of the IoT platform  125 , according to the present disclosure. The IoT platform  125  of the present disclosure is a platform for enterprise performance management that uses real-time accurate models and visual analytics to deliver intelligent actionable recommendations and/or analytics for sustained peak performance of the enterprise  160   a - 160   n . The IoT platform  125  is an extensible platform that is portable for deployment in any cloud or data center environment for providing an enterprise-wide, top to bottom view, displaying the status of processes, assets, people, and safety. Further, the IoT platform  125  supports end-to-end capability to execute digital twins against process data and to translate the output into actionable insights, using the framework  200 , detailed further below. 
     As shown in  FIG.  2   , the framework  200  of the IoT platform  125  comprises a number of layers including, for example, an IoT layer  205 , an enterprise integration layer  210 , a data pipeline layer  215 , a data insight layer  220 , an application services layer  225 , and an applications layer  230 . The IoT platform  125  also includes a core services layer  235  and an extensible object model (EOM)  250  comprising one or more knowledge graphs  251 . The layers  205 - 235  further include various software components that together form each layer  205 - 235 . For example, in one or more embodiments, each layer  205 - 235  includes one or more of the modules  141 , models  142 , engines  143 , databases  144 , services  145 , applications  146 , or combinations thereof. In some embodiments, the layers  205 - 235  are combined to form fewer layers. In some embodiments, some of the layers  205 - 235  are separated into separate, more numerous layers. In some embodiments, some of the layers  205 - 235  are removed while others may be added. 
     The IoT platform  125  is a model-driven architecture. Thus, the extensible object model  250  communicates with each layer  205 - 230  to contextualize site data of the enterprise  160   a - 160   n  using an extensible graph based object model (or “asset model”). In one or more embodiments, the extensible object model  250  is associated with knowledge graphs  251  where the equipment (e.g., edge devices  161   a - 161   n ) and processes of the enterprise  160   a - 160   n  are modeled. The knowledge graphs  251  of EOM  250  are configured to store the models in a central location. The knowledge graphs  251  define a collection of nodes and links that describe real-world connections that enable smart systems. As used herein, a knowledge graph  251 : (i) describes real-world entities (e.g., edge devices  161   a - 161   n ) and their interrelations organized in a graphical interface; (ii) defines possible classes and relations of entities in a schema; (iii) enables interrelating arbitrary entities with each other; and (iv) covers various topical domains. In other words, the knowledge graphs  251  define large networks of entities (e.g., edge devices  161   a - 161   n ), semantic types of the entities, properties of the entities, and relationships between the entities. Thus, the knowledge graphs  251  describe a network of “things” that are relevant to a specific domain or to an enterprise or organization. Knowledge graphs  251  are not limited to abstract concepts and relations, but can also contain instances of objects, such as, for example, documents and datasets. In some embodiments, the knowledge graphs  251  include resource description framework (RDF) graphs. As used herein, a “RDF graph” is a graph data model that formally describes the semantics, or meaning, of information. The RDF graph also represents metadata (e.g., data that describes data). According to various embodiments, knowledge graphs  251  also include a semantic object model. The semantic object model is a subset of a knowledge graph  251  that defines semantics for the knowledge graph  251 . For example, the semantic object model defines the schema for the knowledge graph  251 . 
     As used herein, EOM  250  includes a collection of application programming interfaces (APIs) that enables seeded semantic object models to be extended. For example, the EOM  250  of the present disclosure enables a customer&#39;s knowledge graph  251  to be built subject to constraints expressed in the customer&#39;s semantic object model. Thus, the knowledge graphs  251  are generated by customers (e.g., enterprises or organizations) to create models of the edge devices  161   a - 161   n  of an enterprise  160   a - 160   n , and the knowledge graphs  251  are input into the EOM  250  for visualizing the models (e.g., the nodes and links). 
     The models describe the assets (e.g., the nodes) of an enterprise (e.g., the edge devices  161   a - 161   n ) and describe the relationship of the assets with other components (e.g., the links). The models also describe the schema (e.g., describe what the data is), and therefore the models are self-validating. For example, in one or more embodiments, the model describes the type of sensors mounted on any given asset (e.g., edge device  161   a - 161   n ) and the type of data that is being sensed by each sensor. According to various embodiments, a KPI framework is used to bind properties of the assets in the extensible object model  250  to inputs of the KPI framework. Accordingly, the IoT platform  125  is an extensible, model-driven end-to-end stack including: two-way model sync and secure data exchange between the edge  115  and the cloud  105 , metadata driven data processing (e.g., rules, calculations, and aggregations), and model driven visualizations and applications. As used herein, “extensible” refers to the ability to extend a data model to include new properties/columns/fields, new classes/tables, and new relations. Thus, the IoT platform  125  is extensible with regards to edge devices  161   a - 161   n  and the applications  146  that handle those devices  161   a - 161   n . For example, when new edge devices  161   a - 161   n  are added to an enterprise  160   a - 160   n  system, the new devices  161   a - 161   n  will automatically appear in the IoT platform  125  so that the corresponding applications  146  understand and use the data from the new devices  161   a - 161   n.    
     In some cases, asset templates are used to facilitate configuration of instances of edge devices  161   a - 161   n  in the model using common structures. An asset template defines the typical properties for the edge devices  161   a - 161   n  of a given enterprise  160   a - 160   n  for a certain type of device. For example, an asset template of a pump includes modeling the pump having inlet and outlet pressures, speed, flow, etc. The templates may also include hierarchical or derived types of edge devices  161   a - 161   n  to accommodate variations of a base type of device  161   a - 161   n . For example, a reciprocating pump is a specialization of a base pump type and would include additional properties in the template. Instances of the edge device  161   a - 161   n  in the model are configured to match the actual, physical devices of the enterprise  160   a - 160   n  using the templates to define expected attributes of the device  161   a - 161   n . Each attribute is configured either as a static value (e.g., capacity is 1000 BPH) or with a reference to a time series tag that provides the value. The knowledge graph  251  can automatically map the tag to the attribute based on naming conventions, parsing, and matching the tag and attribute descriptions and/or by comparing the behavior of the time series data with expected behavior. In one or more embodiments, each of the key attribute contributing to one or more metrics to drive a dashboard is marked with one or more metric tags such that a dashboard visualization is generated. 
     The modeling phase includes an onboarding process for syncing the models between the edge  115  and the cloud  105 . For example, in one or more embodiments, the onboarding process includes a simple onboarding process, a complex onboarding process, and/or a standardized rollout process. The simple onboarding process includes the knowledge graph  251  receiving raw model data from the edge  115  and running context discovery algorithms to generate the model. The context discovery algorithms read the context of the edge naming conventions of the edge devices  161   a - 161   n  and determine what the naming conventions refer to. For example, in one or more embodiments, the knowledge graph  251  receives “TMP” during the modeling phase and determine that “TMP” relates to “temperature.” The generated models are then published. The complex onboarding process includes the knowledge graph  251  receiving the raw model data, receiving point history data, and receiving site survey data. According to various embodiments, the knowledge graph  251  then uses these inputs to run the context discovery algorithms. According to various embodiments, the generated models are edited and then the models are published. The standardized rollout process includes manually defining standard models in the cloud  105  and pushing the models to the edge  115 . 
     The IoT layer  205  includes one or more components for device management, data ingest, and/or command/control of the edge devices  161   a - 161   n . The components of the IoT layer  205  enable data to be ingested into, or otherwise received at, the IoT platform  125  from a variety of sources. For example, in one or more embodiments, data is ingested from the edge devices  161   a - 161   n  through process historians or laboratory information management systems. The IoT layer  205  is in communication with the edge connectors  165   a - 165   n  installed on the edge gateways  162   a - 162   n  through network  110 , and the edge connectors  165   a - 165   n  send the data securely to the IoT layer  205 . In some embodiments, only authorized data is sent to the IoT platform  125 , and the IoT platform  125  only accepts data from authorized edge gateways  162   a - 162   n  and/or edge devices  161   a - 161   n . According to various embodiments, data is sent from the edge gateways  162   a - 162   n  to the IoT platform  125  via direct streaming and/or via batch delivery. Further, after any network or system outage, data transfer will resume once communication is re-established and any data missed during the outage will be backfilled from the source system or from a cache of the IoT platform  125 . According to various embodiments, the IoT layer  205  also includes components for accessing time series, alarms and events, and transactional data via a variety of protocols. 
     The enterprise integration layer  210  includes one or more components for events/messaging, file upload, and/or REST/OData. The components of the enterprise integration layer  210  enable the IoT platform  125  to communicate with third party cloud applications  211 , such as any application(s) operated by an enterprise in relation to its edge devices. For example, the enterprise integration layer  210  connects with enterprise databases, such as guest databases, customer databases, financial databases, patient databases, etc. The enterprise integration layer  210  provides a standard application programming interface (API) to third parties for accessing the IoT platform  125 . The enterprise integration layer  210  also enables the IoT platform  125  to communicate with the OT systems  163   a - 163   n  and IT applications  164   a - 164   n  of the enterprise  160   a - 160   n . Thus, the enterprise integration layer  210  enables the IoT platform  125  to receive data from the third-party applications  211  rather than, or in combination with, receiving the data from the edge devices  161   a - 161   n  directly. 
     The data pipeline layer  215  includes one or more components for data cleansing/enriching, data transformation, data calculations/aggregations, and/or API for data streams. Accordingly, in one or more embodiments, the data pipeline layer  215  pre-processes and/or performs initial analytics on the received data. The data pipeline layer  215  executes advanced data cleansing routines including, for example, data correction, mass balance reconciliation, data conditioning, component balancing and simulation to ensure the desired information is used as a basis for further processing. The data pipeline layer  215  also provides advanced and fast computation. For example, cleansed data is run through enterprise-specific digital twins. According to various embodiments, the enterprise-specific digital twins include a reliability advisor containing process models to determine the current operation and the fault models to trigger any early detection and determine an appropriate resolution. According to various embodiments, the digital twins also include an optimization advisor that integrates real-time economic data with real-time process data, selects the right feed for a process, and determines optimal process conditions and product yields. 
     According to various embodiments, the data pipeline layer  215  employs models and templates to define calculations and analytics. Additionally or alternatively, according to various embodiments, the data pipeline layer  215  employs models and templates to define how the calculations and analytics relate to the assets (e.g., the edge devices  161   a - 161   n ). For example, in an embodiment, a pump template defines pump efficiency calculations such that every time a pump is configured, the standard efficiency calculation is automatically executed for the pump. The calculation model defines the various types of calculations, the type of engine that should run the calculations, the input and output parameters, the preprocessing requirement and prerequisites, the schedule, etc. According to various embodiments, the actual calculation or analytic logic is defined in the template or it may be referenced. Thus, according to various embodiments, the calculation model is employed to describe and control the execution of a variety of different process models. According to various embodiments, calculation templates are linked with the asset templates such that when an asset (e.g., edge device  161   a - 161   n ) instance is created, any associated calculation instances are also created with their input and output parameters linked to the appropriate attributes of the asset (e.g., edge device  161   a - 161   n ). 
     According to various embodiments, the IoT platform  125  supports a variety of different analytics models including, for example, first principles models, empirical models, engineered models, user-defined models, machine learning models, built-in functions, and/or any other types of analytics models. Fault models and predictive maintenance models will now be described by way of example, but any type of models may be applicable. 
     Fault models are used to compare current and predicted enterprise  160   a - 160   n  performance to identify issues or opportunities, and the potential causes or drivers of the issues or opportunities. The IoT platform  125  includes rich hierarchical symptom-fault models to identify abnormal conditions and their potential consequences. For example, in one or more embodiments, the IoT platform  125  drill downs from a high-level condition to understand the contributing factors, as well as determining the potential impact a lower level condition may have. There may be multiple fault models for a given enterprise  160   a - 160   n  looking at different aspects such as process, equipment, control, and/or operations. According to various embodiments, each fault model identifies issues and opportunities in their domain, and can also look at the same core problem from a different perspective. According to various embodiments, an overall fault model is layered on top to synthesize the different perspectives from each fault model into an overall assessment of the situation and point to the true root cause. 
     According to various embodiments, when a fault or opportunity is identified, the IoT platform  125  provides recommendations about an optimal corrective action to take. Initially, the recommendations are based on expert knowledge that has been pre-programmed into the system by process and equipment experts. A recommendation services module presents this information in a consistent way regardless of source, and supports workflows to track, close out, and document the recommendation follow-up. According to various embodiments, the recommendation follow-up is employed to improve the overall knowledge of the system over time as existing recommendations are validated (or not) or new cause and effect relationships are learned by users and/or analytics. 
     According to various embodiments, the models are used to accurately predict what will occur before it occurs and interpret the status of the installed base. Thus, the IoT platform  125  enables operators to quickly initiate maintenance measures when irregularities occur. According to various embodiments, the digital twin architecture of the IoT platform  125  employs a variety of modeling techniques. According to various embodiments, the modeling techniques include, for example, rigorous models, fault detection and diagnostics (FDD), descriptive models, predictive maintenance, prescriptive maintenance, process optimization, and/or any other modeling technique. 
     According to various embodiments, the rigorous models are converted from process design simulation. In this manner, process design is integrated with feed conditions and production requirement. Process changes and technology improvement provide business opportunities that enable more effective maintenance schedule and deployment of resources in the context of production needs. The fault detection and diagnostics include generalized rule sets that are specified based on industry experience and domain knowledge and can be easily incorporated and used working together with equipment models. According to various embodiments, the descriptive models identifies a problem and the predictive models determines possible damage levels and maintenance options. According to various embodiments, the descriptive models include models for defining the operating windows for the edge devices  161   a - 161   n.    
     Predictive maintenance includes predictive analytics models developed based on rigorous models and statistic models, such as, for example, principal component analysis (PCA) and partial least square (PLS). According to various embodiments, machine learning methods are applied to train models for fault prediction. According to various embodiments, predictive maintenance leverages FDD-based algorithms to continuously monitor individual control and equipment performance. Predictive modeling is then applied to a selected condition indicator that deteriorates in time. Prescriptive maintenance includes determining an optimal maintenance option and when it should be performed based on actual conditions rather than time-based maintenance schedule. According to various embodiments, prescriptive analysis selects the right solution based on the company&#39;s capital, operational, and/or other requirements. Process optimization is determining optimal conditions via adjusting set-points and schedules. The optimized set-points and schedules can be communicated directly to the underlying controllers, which enables automated closing of the loop from analytics to control. 
     The data insight layer  220  includes one or more components for time series databases (TDSB), relational/document databases, data lakes, blob, files, images, and videos, and/or an API for data query. According to various embodiments, when raw data is received at the IoT platform  125 , the raw data is stored as time series tags or events in warm storage (e.g., in a TSDB) to support interactive queries and to cold storage for archive purposes. According to various embodiments, data is sent to the data lakes for offline analytics development. According to various embodiments, the data pipeline layer  215  accesses the data stored in the databases of the data insight layer  220  to perform analytics, as detailed above. 
     The application services layer  225  includes one or more components for rules engines, workflow/notifications, KPI framework, insights (e.g., actionable insights), decisions, recommendations, machine learning, and/or an API for application services. The application services layer  225  enables building of applications  146   a - d . The applications layer  230  includes one or more applications  146   a - d  of the IoT platform  125 . For example, according to various embodiments, the applications  146   a - d  includes a buildings application  146   a , a plants application  146   b , an aero application  146   c , and other enterprise applications  146   d . According to various embodiments, the applications  146  includes general applications  146  for portfolio management, asset management, autonomous control, and/or any other custom applications. According to various embodiments, portfolio management includes the KPI framework and a flexible user interface (UI) builder. According to various embodiments, asset management includes asset performance and asset health. According to various embodiments, autonomous control includes energy optimization and/or predictive maintenance. As detailed above, according to various embodiments, the general applications  146  is extensible such that each application  146  is configurable for the different types of enterprises  160   a - 160   n  (e.g., buildings application  146   a , plants application  146   b , aero application  146   c , and other enterprise applications  146   d ). 
     The applications layer  230  also enables visualization of performance of the enterprise  160   a - 160   n . For example, dashboards provide a high-level overview with drill downs to support deeper investigations. Recommendation summaries give users prioritized actions to address current or potential issues and opportunities. Data analysis tools support ad hoc data exploration to assist in troubleshooting and process improvement. 
     The core services layer  235  includes one or more services of the IoT platform  125 . According to various embodiments, the core services  235  include data visualization, data analytics tools, security, scaling, and monitoring. According to various embodiments, the core services  235  also include services for tenant provisioning, single login/common portal, self-service admin, UI library/UI tiles, identity/access/entitlements, logging/monitoring, usage metering, API gateway/dev portal, and the IoT platform  125  streams. 
       FIG.  3    illustrates a system  300  that provides an exemplary environment according to one or more described features of one or more embodiments of the disclosure. According to an embodiment, the system  300  includes an asset performance management computer system  302  to facilitate a practical application of data analytics technology and/or digital transformation technology to provide optimization related to enterprise performance management. In one or more embodiments, the asset performance management computer system  302  facilitates a practical application of classification of events by pattern recognition in multivariate time series data associated with one or more assets and/or one or more asset processes. In one or more embodiments, the asset performance management computer system  302  stores and/or analyzes data that is aggregated from one or more assets and/or one or more data sources associated with an enterprise system (e.g., a building system, an industrial system or another type of enterprise system). Additionally, in one or more embodiments, the asset performance management computer system  302  provides data driven automatic labeling of events in the aggregated data using data preprocessing, data augmentation, and/or machine learning classifiers associated with candidate data classification. 
     In an embodiment, the asset performance management computer system  302  is a server system (e.g., a server device) that facilitates a data analytics platform between one or more computing devices, one or more data sources, and/or one or more assets. In one or more embodiments, the asset performance management computer system  302  is a device with one or more processors and a memory. In one or more embodiments, the asset performance management computer system  302  is a computer system from the computer systems  120 . For example, in one or more embodiments, the asset performance management computer system  302  is implemented via the cloud  105 . The asset performance management computer system  302  is also related to one or more technologies, such as, for example, enterprise technologies, connected building technologies, industrial technologies, Internet of Things (IoT) technologies, data analytics technologies, digital transformation technologies, cloud computing technologies, cloud database technologies, server technologies, network technologies, private enterprise network technologies, wireless communication technologies, machine learning technologies, artificial intelligence technologies, digital processing technologies, electronic device technologies, computer technologies, supply chain analytics technologies, aircraft technologies, industrial technologies, cybersecurity technologies, navigation technologies, asset visualization technologies, oil and gas technologies, petrochemical technologies, refinery technologies, process plant technologies, procurement technologies, and/or one or more other technologies. 
     Moreover, the asset performance management computer system  302  provides an improvement to one or more technologies such as enterprise technologies, connected building technologies, industrial technologies, IoT technologies, data analytics technologies, digital transformation technologies, cloud computing technologies, cloud database technologies, server technologies, network technologies, private enterprise network technologies, wireless communication technologies, machine learning technologies, artificial intelligence technologies, digital processing technologies, electronic device technologies, computer technologies, supply chain analytics technologies, aircraft technologies, industrial technologies, cybersecurity technologies, navigation technologies, asset visualization technologies, oil and gas technologies, petrochemical technologies, refinery technologies, process plant technologies, procurement technologies, and/or one or more other technologies. In an implementation, the asset performance management computer system  302  improves performance of a computing device. For example, in one or more embodiments, the asset performance management computer system  302  improves processing efficiency of a computing device (e.g., a server), reduces power consumption of a computing device (e.g., a server), improves quality of data provided by a computing device (e.g., a server), etc. 
     The asset performance management computer system  302  includes a data aggregation component  304 , an event classification component  306  and/or a dashboard visualization component  308 . Additionally, in one or more embodiments, the asset performance management computer system  302  includes a processor  310  and/or a memory  312 . In certain embodiments, one or more aspects of the asset performance management computer system  302  (and/or other systems, apparatuses and/or processes disclosed herein) constitute executable instructions embodied within a computer-readable storage medium (e.g., the memory  312 ). For instance, in an embodiment, the memory  312  stores computer executable component and/or executable instructions (e.g., program instructions). Furthermore, the processor  310  facilitates execution of the computer executable components and/or the executable instructions (e.g., the program instructions). In an example embodiment, the processor  310  is configured to execute instructions stored in the memory  312  or otherwise accessible to the processor  310 . 
     The processor  310  is a hardware entity (e.g., physically embodied in circuitry) capable of performing operations according to one or more embodiments of the disclosure. Alternatively, in an embodiment where the processor  310  is embodied as an executor of software instructions, the software instructions configure the processor  310  to perform one or more algorithms and/or operations described herein in response to the software instructions being executed. In an embodiment, the processor  310  is a single core processor, a multi-core processor, multiple processors internal to the asset performance management computer system  302 , a remote processor (e.g., a processor implemented on a server), and/or a virtual machine. In certain embodiments, the processor  310  is in communication with the memory  312 , the data aggregation component  304 , the event classification component  306  and/or the dashboard visualization component  308  via a bus to, for example, facilitate transmission of data among the processor  310 , the memory  312 , the data aggregation component  304 , the event classification component  306  and/or the dashboard visualization component  308 . The processor  310  may be embodied in a number of different ways and, in certain embodiments, includes one or more processing devices configured to perform independently. Additionally or alternatively, in one or more embodiments, the processor  310  includes one or more processors configured in tandem via a bus to enable independent execution of instructions, pipelining of data, and/or multi-thread execution of instructions. 
     The memory  312  is non-transitory and includes, for example, one or more volatile memories and/or one or more non-volatile memories. In other words, in one or more embodiments, the memory  312  is an electronic storage device (e.g., a computer-readable storage medium). The memory  312  is configured to store information, data, content, one or more applications, one or more instructions, or the like, to enable the asset performance management computer system  302  to carry out various functions in accordance with one or more embodiments disclosed herein. As used herein in this disclosure, the term “component,” “system,” and the like, is a computer-related entity. For instance, “a component,” “a system,” and the like disclosed herein is either hardware, software, or a combination of hardware and software. As an example, a component is, but is not limited to, a process executed on a processor, a processor, circuitry, an executable component, a thread of instructions, a program, and/or a computer entity. 
     In an embodiment, the asset performance management computer system  302  (e.g., the data aggregation component  304  of the asset performance management computer system  302 ) receives asset data  314  from the edge devices  161   a - 161   n . In one or more embodiments, the edge devices  161   a - 161   n  are associated with a portfolio of assets. For instance, in one or more embodiments, the edge devices  161   a - 161   n  include one or more assets in a portfolio of assets. The edge devices  161   a - 161   n  include, in one or more embodiments, one or more databases, one or more assets (e.g., one or more building assets, one or more industrial assets, etc.), one or more IoT devices (e.g., one or more industrial IoT devices), one or more connected building assets, one or more sensors, one or more actuators, one or more processors, one or more computers, one or more valves, one or more pumps (e.g., one or more centrifugal pumps, etc.), one or more motors, one or more compressors, one or more turbines, one or more ducts, one or more heaters, one or more chillers, one or more coolers, one or more boilers, one or more furnaces, one or more heat exchangers, one or more fans, one or more blowers, one or more conveyor belts, one or more vehicle components, one or more cameras, one or more displays, one or more security components, one or more HVAC components, industrial equipment, factory equipment, and/or one or more other devices that are connected to the network  110  for collecting, sending, and/or receiving information. In one or more embodiments, the edge device  161   a - 161   n  include, or is otherwise in communication with, one or more controllers for selectively controlling a respective edge device  161   a - 161   n  and/or for sending/receiving information between the edge devices  161   a - 161   n  and the asset performance management computer system  302  via the network  110 . The asset data  314  includes, for example, connected building data, sensor data, real-time data, historical data, event data, process data, asset data, location data, and/or other data associated with the edge devices  161   a - 161   n . In one or more embodiments, at least a portion of the asset data  314  is associated with one or more asset processes associated with the portfolio of assets. For example, in one or more embodiments, at least a portion of the asset data  314  is generated and/or employed by one or more asset processes associated with the portfolio of assets. 
     In certain embodiments, at least one edge device from the edge devices  161   a - 161   n  incorporates encryption capabilities to facilitate encryption of one or more portions of the asset data  314 . Additionally, in one or more embodiments, the asset performance management computer system  302  (e.g., the data aggregation component  304  of the asset performance management computer system  302 ) receives the asset data  314  via the network  110 . In one or more embodiments, the network  110  is a Wi-Fi network, a Near Field Communications (NFC) network, a Worldwide Interoperability for Microwave Access (WiMAX) network, a personal area network (PAN), a short-range wireless network (e.g., a Bluetooth® network), an infrared wireless (e.g., IrDA) network, an ultra-wideband (UWB) network, an induction wireless transmission network, and/or another type of network. In one or more embodiments, the edge devices  161   a - 161   n  are associated with an industrial environment (e.g., a plant, etc.). Additionally or alternatively, in one or more embodiments, the edge devices  161   a - 161   n  are associated with components of the edge  115  such as, for example, one or more enterprises  160   a - 160   n.    
     In one or more embodiments, the data aggregation component  304  aggregates the asset data  314  from the edge devices  161   a - 161   n . For instance, in one or more embodiments, the data aggregation component  304  can aggregate the asset data  314  into a multivariate time series database  318 . The multivariate time series database  318  is a cache memory (e.g., a database structure) that dynamically stores the asset data  314  based on interval of time and/or asset hierarchy level. For instance, in one or more embodiments, the multivariate time series database  318  stores the asset data  314  for one or more intervals of time (e.g., 1 minute to 12 minutes, 1 hour to 24 hours, 1 day to 31 days, 1 month to 12 months, etc.) and/or for one or more asset hierarchy levels (e.g., asset level, asset zone, building level, building zone, plant level, plant zone, industrial site level, etc.). In a non-limiting embodiment, the multivariate time series database  318  stores the asset data  314  for a first interval of time (e.g., 1 hour to 24 hours minutes) for a first asset (e.g., a first asset hierarchy level), for a second interval of time (e.g., 1 day to 31 days) for the first asset, and for a third interval of time (e.g., 1 month to 12 months) for the first asset. Furthermore, in the non-limiting embodiment, the multivariate time series database  318  stores the asset data  314  for the first interval of time (e.g., 1 hour to 24 hours minutes) for all assets in a connected building (e.g., a second asset hierarchy level), for the second interval of time (e.g., 1 day to 31 days) for all the assets in the connected building, and for the third interval of time (e.g., 1 month to 12 months) for the all the assets in the connected building. In the non-limiting embodiment, the multivariate time series database  318  also stores the asset data  314  for the first interval of time (e.g., 1 hour to 24 hours minutes) for all connected buildings within a particular geographic region (e.g., a third asset hierarchy level), for the second interval of time (e.g., 1 day to 31 days) for all connected buildings within the particular geographic region, and for the third interval of time (e.g., 1 month to 12 months) for all connected buildings within the particular geographic region. Additionally, in one or more embodiments, the multivariate time series database  318  stores at least a portion of the asset data  314  associated with two or more variables (e.g., two or more features) associated with the portfolio of assets. As such, in one or more embodiments, the multivariate time series database  318  stores multivariate data (e.g., multivariate time series data) associated with the one or more assets (e.g., the edge devices  161   a - n ) 
     In one or more embodiments, the data aggregation component  304  repeatedly updates data of the multivariate time series database  318  based on the asset data  314  provided by the edge devices  161   a - 161   n  during the one or more intervals of time associated with the multivariate time series database  318 . For instance, in one or more embodiments, the data aggregation component  304  stores new data and/or modified data associated with the asset data  314 . In one or more embodiments, the data aggregation component  304  repeatedly scans the edge devices  161   a - 161   n  to determine new data for storage in the multivariate time series database  318 . In one or more embodiments, the data aggregation component  304  formats one or more portions of the asset data  314 . For instance, in one or more embodiments, the data aggregation component  304  provides a formatted version of the asset data  314  to the multivariate time series database  318 . In an embodiment, the formatted version of the asset data  314  is formatted with one or more defined formats associated with the one or more intervals of time and/or the one or more asset hierarchy levels. A defined format is, for example, a structure for data fields of the multivariate time series database  318 . In various embodiments, the formatted version of the asset data  314  is stored in the multivariate time series database  318 . 
     In one or more embodiments, the data aggregation component  304  identifies and/or groups data types associated with the asset data  314  based on the one or more intervals of time (e.g., one or more reporting intervals of time), the one or more asset hierarchy levels, and/or corresponding variables (e.g., corresponding features and/or attributes). In one or more embodiments, the data aggregation component  304  employs batching, concatenation of the asset data  314 , identification of data types, merging of the asset data  314 , grouping of the asset data  314 , reading of the asset data  314  and/or writing of the asset data  314  to facilitate storage of the asset data  314  within the multivariate time series database  318 . In one or more embodiments, the data aggregation component  304  groups data from the asset data  314  based on corresponding features and/or attributes of the data. In one or more embodiments, the data aggregation component  304  groups data from the asset data  314  based on corresponding identifiers (e.g., a matching asset hierarchy level, a matching asset, a matching connected building, etc.) for the asset data  314 . In one or more embodiments, the data aggregation component  304  employs one or more locality-sensitive hashing techniques to group data from the asset data  314  based on similarity scores and/or calculated distances between different data in the asset data  314 . 
     In one or more embodiments, the data aggregation component  304  organizes the formatted version of the asset data  314  based on a time series mapping of attributes for the asset data  314 . For instance, in one or more embodiments, the data aggregation component  304  employs a hierarchical data format technique to organize the formatted version of the asset data  314  in the multivariate time series database  318 . In one or more embodiments, the multivariate time series database  318  dynamically stores data (e.g., one or more portions of the asset data  314 ) based on type of data presented via a dashboard visualization. In one or more embodiments, data (e.g., one or more portions of the asset data  314 ) aggregated from the edge devices  161   a - 161   n  is converted into one or more metrics (e.g., a KPI metric, a duty KPI, a duty target KPI) prior to being stored in the multivariate time series database  318 . In one or more embodiments, a metric (e.g. a KP metrics) consists of aspect data indicative of an aspect employed in a model to map an attribute to the metric (e.g., an operating power asset type attribute is mapped to a duty aspect, etc.), aggregation data indicative of information related to aggregation across time, rollup data indicative of an aggregate metric of an asset across an asset at one level as well as across a hierarchy asset, low limit data indicative of a low-limit constant derived from a digital twin model in real-time, high limit data indicative of a high-limit constant derived from a digital twin model in real-time, target data indicative of a target constant derived from a digital twin model in real-time, custom calculation data indicative of information related to custom calculations using aggregate data across time or asset, and/or other data related to the metric. 
     In one or more embodiments, at least a portion of the asset data  314  and/or data stored in the multivariate time series database  318  is employed as training data for one or more machine learning models. For example, in one or more embodiments, at least a portion of the asset data  314  and/or data stored in the multivariate time series database  318  is employed as training data for one or more one or more machine learning classifiers to facilitate classification of one or more events. 
     Additionally or alternatively, in one or more embodiments, the data aggregation component  304  is configured to perform data preprocessing with respect to the asset data  314  and/or data stored in the multivariate time series database  318 . The data preprocessing includes, for example, reading the asset data  314  and/or data stored in the multivariate time series database  318  via one or more data pipelines. In one or more embodiments, the data aggregation component  304  performs one or more data cleaning processes to clean the asset data  314  and/or data stored in the multivariate time series database  318 . In an embodiment, the data aggregation component  304  removes undefined data (e.g., Not a Number (NaN) values, etc.) from the asset data  314  and/or data stored in the multivariate time series database  318 . In another embodiment, the data aggregation component  304  additionally or alternatively removes redundant columns (e.g., redundant columns associated with constant data values) from the formatted version of the asset data  314  and/or data stored in the multivariate time series database  318 . Additionally, in one or more embodiments, the data aggregation component  304  transforms one or more portions of the asset data  314  and/or data stored in the multivariate time series database  318  into features (e.g., attributes). For example, in one or more embodiments, the data aggregation component  304  groups the asset data  314  and/or data stored in the multivariate time series database  318  into batches associated with respective fixed time intervals and extracts statistical features (e.g., statistical attributes) from the respective batch groups. Additionally, in one or more embodiments, the data aggregation component  304  further transforms (e.g., scales) one or more portions of the asset data  314  and/or data stored in the multivariate time series database  318  by adjusting a range of the data. Additionally, in one or more embodiments, the data aggregation component  304  further transforms (e.g., normalizes) one or more portions of the asset data  314  and/or data stored in the multivariate time series database  318  by scaling the data and/or generating a normal distribution (e.g., a Gaussian distribution) with respect to the data. In one or more embodiments, the data aggregation component  304  additionally generates data interaction columns for the asset data  314  and/or data stored in the multivariate time series database  318  to provide additional data to facilitate training and/or machine learning. For example, in one or more embodiments, the data aggregation component  304  generates one or more additional columns for the formatted version of the asset data  314  and/or data stored in the multivariate time series database  318  by multiplying two or more existing columns in the formatted version of the asset data  314  and/or data stored in the multivariate time series database  318 . 
     The event classification component  306  is configured to classify one or more events associated with the asset data  314  and/or data stored in the multivariate time series database  318 . In one or more embodiments, the event classification component  306  performs data augmentation with respect to the asset data  314  and/or data stored in the multivariate time series database  318  to facilitate classifying the one or more events. In one or more embodiments, the event classification component  306  performs the data augmentation to provide suitable data to train one or more machine learning classifiers to classify the one or more events. In one or more embodiments, the event classification component  306  employs one or more data augmentation techniques to augment the asset data  314  and/or data stored in the multivariate time series database  318  based on data associated with a predicted rare event and/or rule weighting. The one or more data augmentation techniques include, for example, a synthetic data augmentation for tabular data (SMOTE) technique that employs a K-Nearest Neighbors (KNN) technique with respect to data, an adaptive synthetic sampling approach (ADASYN) technique that employs a distribution to weight data, a support vector machine (SVM)-SMOTE technique that employs one or more SVMs in combination with the KNN technique, a SMOTE-TOMEK technique that removes data points of a majority class and/or adds data points for a minority class using SMOTE, a boosting based technique (e.g., a SMOTEBoost technique, a RareBoost technique, etc.), a cost sensitive classification technique (e.g., MetaCost, AdaCost, CSB, SSTBoost, etc.), a clustering based classification technique, an over-sampling a rare class technique, a linear regression model technique for increasing minority class samples, and/or another type of data augmentation technique. 
     In one or more embodiments, the event classification component  306  is configured to train one or more machine learning classifiers. For instance, in one or more embodiments, the event classification component  306  determines and/or tunes one or more parameters (e.g., one or more hyperparameters, one or more weights, etc.) for one or more learning processes associated with the one or more machine learning classifiers. In an example embodiment where a machine learning classifier is a random forest classifier, the event classification component  306  determines and/or tunes one or more parameters (e.g., one or more hyperparameters, one or more weights, etc.) during training of the random forest classifier. In another example embodiment, the one or more parameters (e.g., one or more hyperparameters, one or more weights, etc.) includes a parameter associated with a number of processors to be employed by a machine learning classifier, a number of processing trees to be included in a machine learning classifier, a number of split points for a processing tree included in a machine learning classifier, a number of samples to be included in data for a machine learning classifier, a size of a node in a machine learning classifier, a number of random samples to be included in data for a machine learning classifier, and/or one or more other parameters for a machine learning classifier. Based on the one or more parameters (e.g., one or more hyperparameters, one or more weights, etc.) determined and/or tuned by the event classification component  306 , in one or more embodiments, the event classification component  306  trains the respective machine learning classifier to detect presence or absence of a particular event associated with a defined data signature. In one or more embodiments, the event classification component  306  trains and/or generates respective machine learning classifiers for respective defined data signatures. In one or more embodiments, a trained version of a machine learning classifier is configured with, for example, one or more decision rules, one or more decision trees, a classification type associated with a set of features, and/or a model score for a particular classification type. In one or more embodiments, a trained version of a machine learning classifier is correlated with a score (e.g., a quality score, an F1 score, a recall score, a precision score, a correlation score, a Matthews correlation coefficient (MCC) score, and/or another type of scoring metric). 
     In one or more embodiments, the event classification component  306  determines accuracy of a classified event by analyzing data prior to time series data associated with a classified event and/or data after time series data associated with a classified event. For example, in one or more embodiments, the event classification component  306  determines slope, an intercept, and/or a degree of oscillation associated with data prior to time series data associated with a classified event and/or data after time series data associated with a classified event. Additionally, the event classification component  306  compares the determined slope, intercept, and/or degree of oscillation associated with the classified event with another slope, another intercept, and/or another degree of oscillation associated with another classified event to determine whether noise data and/or statistical parameters associated with the classified events are within a certain degree of similarity. If so, the classified event is determined to be an accurately classified event. However, if not, the classified event is determined to not be an accurately classified event. 
     In one or more embodiments, the asset performance management computer system  302  (e.g., the event classification component  306  of the asset performance management computer system  302 ) receives a request  320 . In an embodiment, the request  320  is a request to classify one or more events associated with one or more assets in a portfolio of assets. For example, in an embodiment, the request  320  is a request to classify one or more events associated with the edge devices  161   a - n . In one or more embodiments, the request  320  is a request to classify one or more events in the asset data  314  and/or multivariate data stored in the multivariate time series database  318 . In one or more embodiments, the request  320  includes a request to generate a dashboard visualization associated with the one or more assets in the portfolio of assets (e.g., the edge devices  161   a - 161   n  included in a portfolio of assets). In one or more embodiments, the request  320  is received in response to an action (e.g., a user-initiated action, modification of an interactive graphical element, etc.) initiated via an electronic interface of a computing device. In one or more embodiments, the request  320  is received in response to an action initiated via a processing unit (e.g., an edge device, a controller, etc.) associated with the one or more assets. In one or more embodiments, the request  320  is received in response to an asset schedule satisfying a defined criterion (e.g., an asset schedule interval being above a threshold timer). In one or more embodiments, the request  320  is received in response to detection of a possible anomaly associated with an asset (e.g., in response to an anomaly index for an asset being greater than a specified threshold level). 
     In one or more embodiments, the request  320  includes one or more asset descriptors that describe one or more assets in the portfolio of assets. For instance, in one or more embodiments, the request  320  includes one or more asset descriptors that describe the edge devices  161   a - 161   n . An asset descriptor includes, for example, an asset name, an asset identifier, an asset level and/or other information associated with an asset. Additionally or alternatively, in one or more embodiments, the request  320  includes one or more user identifiers describing a user role for a user associated with access of a dashboard visualization. A user identifier includes, for example, an identifier for a user role name (e.g., a manager, an executive, a maintenance engineer, a process engineer, etc.). Additionally or alternatively, in one or more embodiments, the request  320  includes one or more metrics context identifiers describing context for the metrics. A metrics context identifier includes, for example, an identifier for a plant performance metric, an asset performance metric, a goal (e.g., review production related to one or more assets, etc.). Additionally or alternatively, in one or more embodiments, the request  320  includes one or more time interval identifiers describing an interval of time associated with at least a portion of aggregated multivariate data stored in the multivariate time series database  318 . A time interval identifier describes, for example, an interval of time for aggregated data such as hourly, daily, monthly, yearly etc. In one or more embodiments, a time interval identifier is a reporting time identifier describing an interval of time for the metrics. Additionally or alternatively, in one or more embodiments, the request  320  includes a machine learning classifier identifier describing a type of machine learning classifier and/or a type of classification algorithm (e.g., random forest, weighted SVM, KNN, long short term memory (LSTM), etc.) to employ to classify the one or more events in the asset data  314  and/or multivariate data stored in the multivariate time series database  318 . 
     In response to the request  320 , the event classification component  306  obtains aggregated multivariate data associated with the one or more assets. For example, in one or more embodiments, the event classification component  306  obtains the aggregated multivariate data from the multivariate time series database  318  and/or directly from the edge devices  161   a - n . Additionally, in one or more embodiments, the event classification component  306  determines one or more data signatures associated with the aggregated multivariate data. The one or more data signatures are, for example, one or more digital patterns (e.g., one or more digital fingerprints) that correspond to one or more candidate events associated with one or more defined event attributes. In an embodiment, a data signature corresponds to an aggregated data pattern associated with one or more sensor outputs of one or more assets in an industrial environment. In another embodiment, a data signature corresponds to an aggregated data pattern associated with one or more motors of one or more assets in a warehouse environment. In another embodiment, a data signature corresponds to an aggregated data pattern associated with environmental data related to one or more assets in a building environment. For example, in one or more embodiments, the event classification component  306  determines a data signature for at least a portion of the aggregated multivariate data that corresponds to sensor output of the one or more assets, a data pattern for one or more motors related to the one or more assets, and/or environmental data related to the one or more assets. In one or more embodiments, the event classification component  306  obtains the aggregated multivariate data based on the one or more time interval identifiers and/or the user identifier. 
     The event classification component  306  is configured to label one or more events associated with the multivariate data. In one or more embodiments, the event classification component  306  is configured to label the one or more events associated with the multivariate data based on respective defined data signatures for respective defined events associated with a defined event attribute. For example, in one or more embodiments, the event classification component  306  is configured to determine, based on respective defined data signatures for respective defined events associated with a defined event attribute, a respective label for one or more events associated with the multivariate data. The respective defined data signatures are, for example, one or more defined digital patterns (e.g., one or more defined digital fingerprints) that correspond to one or more historical events and/or one or more predetermined events associated with one or more defined event attributes. In one or more embodiments, the respective defined data signatures are generated in response to the respective defined events satisfying a defined ratio of event features to event outcomes. For example, a ratio of event features to event outcomes can define a particular type of event (e.g., a rare event) associated with an asset. In one or more embodiments, the event classification component  306  is configured to determine, based on respective defined data signatures for respective defined events associated with a defined event attribute, a respective label for one or more events associated with a data signature (e.g., a data signature for at least a portion of the aggregated multivariate data that corresponds to sensor output of the one or more assets, a data pattern for one or more motors related to the one or more assets, and/or environmental data related to the one or more assets). In one or more embodiments, the event classification component  306  is configured to determine the respective label based on a comparison between the data signature and the respective defined data signatures. For example, in one or more embodiments, the event classification component  306  is configured to determine the respective label based on a comparison between respective attributes and/or data patterns of the data signature and the respective defined data signatures. 
     In one or more embodiment, in response to the request  320 , the dashboard visualization component  308  generates dashboard visualization data  322  associated with the one or more events. For instance, in one or more embodiments, the dashboard visualization component  308  provides the dashboard visualization to an electronic interface of a computing device based on the dashboard visualization data  322 . In one or more embodiments, the dashboard visualization data  322  and/or the dashboard visualization associated with the dashboard visualization data  322  includes data associated with the one or more events. In one or more embodiments, the dashboard visualization data  322  and/or the dashboard visualization associated with the dashboard visualization data  322  includes one or more metrics associated with the one or more events. 
     Additionally, in one or more embodiments, the dashboard visualization component  308  performs one or more actions based on the one or more events. For instance, in one or more embodiments, the dashboard visualization component  308  generates dashboard visualization data  322  associated with the one or more events. In an embodiment, an action includes generating a user-interactive electronic interface that renders a visual representation of data associated with the one or more events. In another embodiment, an action from the one or more actions includes transmitting, to a computing device, one or more notifications associated with the one or more events. In another embodiment, an action from the one or more actions includes providing an optimal process condition for an asset associated with the asset data  314  based on the one or more events. For example, in another embodiment, an action from the one or more actions includes adjusting a set-point and/or a schedule for an asset associated with the asset data  314  based on the one or more events. In another embodiment, an action from the one or more actions includes one or more corrective action to take for an asset associated with the asset data  314  based on the one or more events. In another embodiment, an action from the one or more actions includes providing an optimal maintenance option for an asset associated with the asset data  314  based on the one or more events. In another embodiment, an action from the one or more actions includes an action associated with the application services layer  225 , the applications layer  230 , and/or the core services layer  235  based on the one or more events. In certain embodiments, an action from the one or more actions includes configuring the dashboard visualization to allow a user to modify a label for an event from the one or more events and/or to generate a new label for the event via the electronic interface. In certain embodiments, an action from the one or more actions includes, in response to generating the new label for the event and/or a determination that the new label satisfies a defined criterion with respect to historical labels, transmitting a notification to the electronic interface to indicate likelihood of labeling accuracy. In certain embodiments, an action from the one or more actions includes retraining one or more machine learning classifiers associated with the respective defined data signatures based on the one or more events. For example, in certain embodiments, one or more weights and/or one or more parameters for a machine learning classifier associated with a defined data signature is updated based on the one or more events. In certain embodiments, an action from the one or more actions includes configuring the dashboard visualization (e.g., based on the one or more events) to provide individual control of the one or more assets via the dashboard visualization. For example, in certain embodiments, the dashboard visualization is configured to receive input from a user to modify one or more parameters of one or more assets. In another example, in certain embodiments, the dashboard visualization is configured to present a notification to allow acceptances of one or more changes related to one or more assets. In another example, in certain embodiments, the dashboard visualization is configured to present a digital twin visualization of one or more assets to provide individual control of the one or more assets via the dashboard visualization. In certain embodiments, an action from the one or more actions includes configuring the dashboard visualization (e.g., based on the one or more events) to facilitate creation of one or more work orders for the one or more assets. 
       FIG.  4    illustrates an exemplary event classification component (e.g., event classification component  306 ) according to one or more described features of one or more embodiments of the disclosure. In one or more embodiments, the event classification component  306  includes a data augmentation component  402 , a training component  404 , and/or a candidate data component  406 . In one or more embodiments, the data augmentation component  402  performs data augmentation with respect to the asset data  314  and/or data stored in the multivariate time series database  318  to facilitate classifying the one or more events. In one or more embodiments, the data augmentation component  402  performs the data augmentation to provide suitable data to train one or more machine learning classifiers to classify the one or more events. In one or more embodiments, the data augmentation component  402  employs one or more data augmentation techniques to augment the asset data  314  and/or data stored in the multivariate time series database  318  based on data associated with a predicted rare event and/or rule weighting. The one or more data augmentation techniques include, for example, a SMOTE technique that employs a KNN technique with respect to data, an ADASYN technique that employs a distribution to weight data, an SVM-SMOTE technique that employs one or more SVMs in combination with the KNN technique, a SMOTE-TOMEK technique that removes data points of a majority class and/or adds data points for a minority class using SMOTE, a boosting based technique (e.g., a SMOTEBoost technique, a RareBoost technique, etc.), a cost sensitive classification technique (e.g., MetaCost, AdaCost, CSB, SSTBoost, etc.), a clustering based classification technique, an over-sampling a rare class technique, a linear regression model technique for increasing minority class samples, and/or another type of data augmentation technique. 
     In one or more embodiments, the training component  404  is configured to train one or more machine learning classifiers. For instance, in one or more embodiments, the training component  404  determines and/or tunes one or more parameters (e.g., one or more hyperparameters, one or more weights, etc.) for one or more learning processes associated with the one or more machine learning classifiers. In an example embodiment where a machine learning classifier is a random forest classifier, the training component  404  determines and/or tunes one or more parameters (e.g., one or more hyperparameters, one or more weights, etc.) during training of the random forest classifier. In another example embodiment, the one or more parameters (e.g., one or more hyperparameters, one or more weights, etc.) includes a parameter associated with a number of processors to be employed by a machine learning classifier, a number of processing trees to be included in a machine learning classifier, a number of split points for a processing tree included in a machine learning classifier, a number of samples to be included in data for a machine learning classifier, a size of a node in a machine learning classifier, a number of random samples to be included in data for a machine learning classifier, and/or one or more other parameters for a machine learning classifier. Based on the one or more parameters (e.g., one or more hyperparameters, one or more weights, etc.) determined and/or tuned by training component  404 , in one or more embodiments, the training component  404  trains the respective machine learning classifier to detect presence or absence of a particular event associated with a defined data signature. In one or more embodiments, the training component  404  trains and/or generates respective machine learning classifiers for respective defined data signatures. In one or more embodiments, a trained version of a machine learning classifier is configured with, for example, one or more decision rules, one or more decision trees, a classification type associated with a set of features, and/or a model score for a particular classification type. In one or more embodiments, a trained version of a machine learning classifier is correlated with a score (e.g., a quality score, an F1 score, a recall score, a precision score, a correlation score, an MCC score, and/or another type of scoring metric). 
     In one or more embodiments, the candidate data component  406  is configured for candidate data classification of one or more events. In one or more embodiments, the candidate data component  406  executes one or more machine learning classifiers associated with respective defined events to provide a classification of the one or more events. In one or more embodiments, the candidate data component  406  employs one or more data historians to provide incremental classification with respect to one or more machine learning classifiers. In one or more embodiments, the candidate data component  406  derives a feature space for an event based on a feature space employed during training of a machine learning classifier associated with the event. In one or more embodiments, the candidate data component  406  executes a machine learning classifier for a defined event based on parameter obtained during training of the machine learning classifier. In one or more embodiments, the candidate data component  406  correlates a machine learning classifier for a defined event with a quality score (e.g., a goodness of fit score). 
       FIG.  5    illustrates a system  300 ′ that provides an exemplary environment according to one or more described features of one or more embodiments of the disclosure. In an embodiment, the system  300 ′ corresponds to an alternate embodiment of the system  300  shown in  FIG.  3   . According to an embodiment, the system  300 ′ includes the asset performance management computer system  302 , the edge devices  161   a - 161   n , the multivariate time series database  318  and/or a computing device  502 . In one or more embodiments, the asset performance management computer system  302  is in communication with the edge devices  161   a - 161   n  and/or the computing device  502  via the network  110 . The computing device  502  is a mobile computing device, a smartphone, a tablet computer, a mobile computer, a desktop computer, a laptop computer, a workstation computer, a wearable device, a virtual reality device, an augmented reality device, or another type of computing device located remote from the asset performance management computer system  302 . 
     In one or more embodiments, the dashboard visualization component  308  communicates the dashboard visualization data  322  to the computing device  502 . For example, in one or more embodiments, the dashboard visualization data  322  includes one or more visual elements for a visual display (e.g., a user-interactive electronic interface) of the computing device  502  that renders a visual representation of the data associated with the one or more events. In certain embodiments, the visual display of the computing device  502  displays one or more graphical elements associated with the dashboard visualization data  322  (e.g., the data associated with the one or more events). In another example, in one or more embodiments, the dashboard visualization data  322  includes one or notifications associated with the one or more events. In one or more embodiments, the dashboard visualization data  322  allows a user associated with the computing device  502  to make decisions and/or perform one or more actions with respect to the one or more events. In one or more embodiments, the dashboard visualization data  322  allows a user associated with the computing device  502  to control the one or more portions of the one or more assets (e.g., one or more portions of the edge devices  161   a - 161   n ). In one or more embodiments, the dashboard visualization data  322  allows a user associated with the computing device  502  to generate one or more work orders for the one or more assets. 
       FIG.  6    illustrates a system  600  according to one or more embodiments of the disclosure. The system  600  includes the computing device  502 . In one or more embodiments, the computing device  502  employs mobile computing, augmented reality, cloud-based computing, IoT technology and/or one or more other technologies to provide performance data, video, audio, text, graphs, charts, real-time data, graphical data, one or more communications, one or more messages, one or more notifications, and/or other media data associated with the one or more metrics. The computing device  502  includes mechanical components, electrical components, hardware components and/or software components to facilitate determining prioritized actions and/or one or more metrics associated with the asset data  314 . In the embodiment shown in  FIG.  5   , the computing device  502  includes a visual display  604 , one or more speakers  606 , one or more cameras  608 , one or more microphones  610 , a global positioning system (GPS) device  612 , a gyroscope  614 , one or more wireless communication devices  616 , and/or a power supply  618 . 
     In an embodiment, the visual display  604  is a display that facilitates presentation and/or interaction with one or more portions of the dashboard visualization data  322 . In one or more embodiments, the computing device  502  displays an electronic interface (e.g., a graphical user interface) associated with an asset performance management platform. In one or more embodiments, the visual display  604  is a visual display that renders one or more interactive media elements via a set of pixels. The one or more speakers  606  include one or more integrated speakers that project audio. The one or more cameras  608  include one or more cameras that employ autofocus and/or image stabilization for photo capture and/or real-time video. The one or more microphones  610  include one or more digital microphones that employ active noise cancellation to capture audio data. The GPS device  612  provides a geographic location for the computing device  502 . The gyroscope  614  provides an orientation for the computing device  502 . The one or more wireless communication devices  616  includes one or more hardware components to provide wireless communication via one or more wireless networking technologies and/or one or more short-wavelength wireless technologies. The power supply  618  is, for example, a power supply and/or a rechargeable battery that provides power to the visual display  604 , the one or more speakers  606 , the one or more cameras  608 , the one or more microphones  610 , the GPS device  612 , the gyroscope  614 , and/or the one or more wireless communication devices  616 . In certain embodiments, the dashboard visualization data  322  associated with the prioritized actions and/or the one or more metrics is presented via the visual display  604  and/or the one or more speakers  606 . 
       FIG.  7    illustrates an exemplary electronic interface  700  according to one or more embodiments of the disclosure. In an embodiment, the electronic interface  700  is an electronic interface of the computing device  502  that is presented via the visual display  604 . In one or more embodiments, a dashboard visualization is presented via the electronic interface  700 . In certain embodiments, the data visualization presented via the electronic interface  700  presents an asset analytics tool to facilitate event labeling to label a data signature  702  related to asset data (e.g., multivariate data). In an embodiment, the data signature  702  corresponds to an event signature (e.g., a fault signature) for an asset and/or an asset process associated with the asset data. In one or more embodiments, a machine learning classifier is trained based on the data signature  702 . In one or more embodiments, the data signature  702  is associated with an interval of time that begins at time A and ends at time B. In one or more embodiments, the data signature  702  is associated with root cause variable(s) for the data signature  702 . 
       FIG.  8    illustrates an exemplary electronic interface  800  according to one or more embodiments of the disclosure. In an embodiment, the electronic interface  800  is an electronic interface of the computing device  502  that is presented via the visual display  604 . In one or more embodiments, a dashboard visualization is presented via the electronic interface  800 . In certain embodiments, the data visualization presented via the electronic interface  800  presents an asset analytics tool to facilitate event labeling to label the data signature  702 . In one or more embodiments, the electronic interface  800  includes an interactive graphical element  802  that facilitates selection of a type of data signature label for the data signature  702 . In certain embodiments, the interactive graphical element  802  provides configuration capabilities for a timeframe, a mode (e.g., a machine learning mode, a type of event classification etc.), asset information, a label, and/or other information related to the data signature  702 . In certain embodiments, the interactive graphical element  802  is configured to initiate generation of the request  320 . 
       FIG.  9    illustrates an exemplary electronic interface  900  according to one or more embodiments of the disclosure. In an embodiment, the electronic interface  900  is an electronic interface of the computing device  502  that is presented via the visual display  604 . In one or more embodiments, a dashboard visualization is presented via the electronic interface  900 . In certain embodiments, the data visualization presented via the electronic interface  900  presents an asset analytics tool to facilitate event labeling to label the data signature  702 . In one or more embodiments, the electronic interface  900  includes an interactive graphical element  902  that provides data signature details for the data signature  702  such as, for example, a name of the data signature  702 , a start time for the data signature  702 , an end time for the data signature  702 , and/or a description of the data signature  702 . In certain embodiments, the interactive graphical element  902  additionally or alternatively provides configuration capabilities for a timeframe, a mode (e.g., a machine learning mode, a type of event classification etc.), asset information, a label, and/or other information related to the data signature  702 . In certain embodiments, the interactive graphical element  902  is configured to initiate generation of the request  320 . 
       FIG.  10    illustrates an exemplary electronic interface  1000  according to one or more embodiments of the disclosure. In an embodiment, the electronic interface  1000  is an electronic interface of the computing device  502  that is presented via the visual display  604 . In one or more embodiments, a dashboard visualization is presented via the electronic interface  1000 . In certain embodiments, the data visualization presented via the electronic interface  1000  presents an asset analytics tool to facilitate event labeling to label the data signature  702 . In one or more embodiments, the electronic interface  1000  includes an interactive graphical element  1002  that allows a user to specify one or more model variables contributing to the data signature  702  by selecting the one or more model variables from an impact variables list included in the interactive graphical element  1002 . In certain embodiments, the interactive graphical element  1002  provides configuration capabilities for a timeframe, a mode (e.g., a machine learning mode, a type of event classification etc.), asset information, a label, selection of one or more asset variables, a data signature template, and/or other information related to the data signature  702 . In certain embodiments, the interactive graphical element  1002  is configured to initiate generation of the request  320 . In certain embodiments, one or more portions of the electronic interface  1000  is configured with a visual indicator (e.g., a color) related to asset data to indicate a fault, an event, and/or a mode associated with one or more assets. 
       FIG.  11    illustrates a method  1100  for classification of events using pattern recognition in multivariate data, in accordance with one or more embodiments described herein. The method  1100  is associated with the asset performance management computer system  302 , for example. For instance, in one or more embodiments, the method  1100  is executed at a device (e.g. the asset performance management computer system  302 ) with one or more processors and a memory. In one or more embodiments, the method  1100  begins at block  1102  that receives (e.g., by the event classification component  306  and/or the dashboard visualization component  308 ) a request to classify events associated with one or more assets, the request comprising an asset descriptor describing the one or more assets. The request provides one or more technical improvements such as, but not limited to, facilitating interaction with a computing device and/or extended functionality for a computing device. In one or more embodiments, the receiving the request includes receiving the request in response to an action initiated via the electronic interface of the computing device. In one or more embodiments, the receiving the request includes receiving the request in response to an action initiated via a processing unit associated with the one or more assets. 
     At block  1104 , it is determined whether the request is processed. If no, block  1104  is repeated to determine whether the request is processed. If yes, the method  1100  proceeds to block  1106 . In response to the request, block  1106  that obtains, based on the asset descriptor, aggregated multivariate data associated with the one or more assets. The obtaining provides one or more technical improvements such as, but not limited to, extended functionality for a computing device. 
     In response to the request, the method  1100  also includes a block  1108  that labels (e.g., by the event classification component  306 ) one or more events associated with the aggregated multivariate data based on respective defined data signatures for respective defined events associated with a defined event attribute. In one or more embodiments, the labeling includes determining, based on respective defined data signatures for respective defined events associated with a defined event attribute, a respective label for one or more events associated with the aggregated multivariate data. The labeling provides one or more technical improvements such as, but not limited to, improving accuracy of the dashboard visualization. In one or more embodiments, the obtaining the aggregated multivariate data includes determining one or more data signatures associated with the aggregated multivariate data, and the labeling includes labeling the one or more events based on a comparison between the one or more data signatures and the respective defined data signatures. In one or more embodiments, the request further comprises a time interval identifier describing an interval of time associated with the aggregated multivariate data, and the method  1100  further includes selecting the respective defined data signatures based on the time interval identifier. In one or more embodiments, the request further comprises a user identifier describing a user role for a user associated with access of the dashboard visualization via the electronic interface, and the obtaining the aggregated multivariate data includes obtaining the aggregated multivariate data based on the user identifier. In one or more embodiments, the request further comprises a metrics context identifier describing context for metrics associated with the events, and the obtaining the aggregated multivariate data includes obtaining the aggregated multivariate data based on the metrics context identifier. In one or more embodiments, the labeling includes performing one or more pattern recognition processes between the aggregated multivariate data and the respective defined data signatures. For example, in one or more embodiments, the labeling comprises identifying one or more patterns between the aggregated multivariate data and the respective defined data signatures. 
     In response to the request, the method  1100  also includes a block  1110  that provides (e.g., by the dashboard visualization component  308 ) a dashboard visualization to an electronic interface of a computing device, the dashboard visualization comprising data associated with the one or more events. The providing the dashboard visualization with the data associated with the one or more events provides one or more technical improvements such as, but not limited to, what and/or how to present information via a computing device. In one or more embodiments, the method  1100  additionally or alternatively includes configuring the dashboard visualization based on the user identifier. In one or more embodiments, the method  1100  additionally or alternatively includes altering one or more visual elements of the dashboard visualization based on the one or more events. In one or more embodiments, the method  1100  additionally or alternatively includes altering a visual representation of the data associated with the one or more events based on user input data provided via the electronic interface. In one or more embodiments, the providing the dashboard visualization includes displaying one or more metrics associated with the one or more events. In one or more embodiments, the method  1100  additionally or alternatively includes configuring the dashboard visualization to provide individual control of the one or more assets via the dashboard visualization. In one or more embodiments, the method  1100  additionally or alternatively includes configuring the dashboard visualization to facilitate creation of one or more work orders for the one or more assets. 
     In one or more embodiments, the method  1100  additionally or alternatively includes determining a data signature for at least a portion of the aggregated multivariate data that corresponds to sensor output of the one or more assets, data pattern for one or more motors related to the one or more assets, and/or environmental data related to the one or more assets. Additionally, in one or more embodiments, the method  1100  includes determining, based on respective defined data signatures for respective defined events associated with a defined event attribute, a respective label for one or more events associated with the data signature. In one or more embodiments, the method  1100  additionally or alternatively includes determining the respective label based on a comparison between the data signature and the respective defined data signatures. 
     In one or more embodiments, the method  1100  additionally or alternatively includes generating the respective defined data signatures in response to the respective defined events satisfying statistical event data associated with event features and event outcomes. 
     In one or more embodiments, the method  1100  additionally or alternatively includes generating the respective defined data signatures in response to the respective defined events satisfying a defined ratio of event features to event outcomes. 
     In one or more embodiments, the method  1100  additionally or alternatively includes generating a new defined data signature for a new defined event based on a data augmentation technique associated with the respective defined data signatures. For example, in one or more embodiments, the method  1100  additionally or alternatively includes generating a new defined data signature for a new defined event by augmenting one or more portions of a defined data signature. 
     In one or more embodiments, the method  1100  additionally or alternatively includes generating training data for machine learning classifier associated with a defined event based on a data augmentation technique associated with data for the respective defined data signatures. For example, in one or more embodiments, the method  1100  additionally or alternatively includes generating training data for a machine learning classifier associated with a defined event by augmenting one or more portions of a defined data signature. Additionally or alternatively, in one or more embodiments, the method  1100  includes training the machine learning classifier based on the training data associated with the one or more augmented portions of the defined data signature. 
     In one or more embodiments, the method  1100  additionally or alternatively includes augmenting one or more portions of the aggregated multivariate data by combining a first data field of the aggregated multivariate data and a second data field of the aggregated multivariate data in response to a determination that data from the first data field and the second data field satisfy data interaction criterion. Additionally or alternatively, in one or more embodiments, the method  1100  includes storing one or more portions of the augmented aggregated multivariate data in a multivariate time series database. 
     In one or more embodiments, the method  1100  additionally or alternatively includes modifying one or more portions of the aggregated multivariate data by removing a first data field of the aggregated multivariate data in response to a determination that data from the first data field corresponds to the data from a second data field of the aggregated multivariate data. 
     In one or more embodiments, the method  1100  additionally or alternatively includes modifying a label for an event from the one or more events to generate a new label for the event via the electronic interface of the computing device. 
     In one or more embodiments, the method  1100  additionally or alternatively includes, in response to generating the new label for the event and a determination that the new label satisfies a defined criterion with respect to historical labels, transmitting a notification to the electronic interface to indicate likelihood of labeling accuracy. 
     In one or more embodiments, the method  1100  additionally or alternatively includes retraining one or more machine learning classifiers associated with the respective defined data signatures based on the one or more events. 
       FIG.  12    depicts an example system  1200  that may execute techniques presented herein.  FIG.  12    is a simplified functional block diagram of a computer that may be configured to execute techniques described herein, according to exemplary embodiments of the present disclosure. Specifically, the computer (or “platform” as it may not be a single physical computer infrastructure) may include a data communication interface  1260  for packet data communication. The platform also may include a central processing unit (“CPU”)  1220 , in the form of one or more processors, for executing program instructions. The platform may include an internal communication bus  1210 , and the platform also may include a program storage and/or a data storage for various data files to be processed and/or communicated by the platform such as ROM  1230  and RAM  1240 , although the system  1200  may receive programming and data via network communications. The system  1200  also may include input and output ports  1250  to connect with input and output devices such as keyboards, mice, touchscreens, monitors, displays, etc. Of course, the various system functions may be implemented in a distributed fashion on a number of similar platforms, to distribute the processing load. Alternatively, the systems may be implemented by appropriate programming of one computer hardware platform. 
     The foregoing method descriptions and the process flow diagrams are provided merely as illustrative examples and are not intended to require or imply that the steps of the various embodiments must be performed in the order presented. As will be appreciated by one of skill in the art the order of steps in the foregoing embodiments can be performed in any order. Words such as “thereafter,” “then,” “next,” etc. are not intended to limit the order of the steps; these words are simply used to guide the reader through the description of the methods. Further, any reference to claim elements in the singular, for example, using the articles “a,” “an” or “the” is not to be construed as limiting the element to the singular. 
     It is to be appreciated that ‘one or more’ includes a function being performed by one element, a function being performed by more than one element, e.g., in a distributed fashion, several functions being performed by one element, several functions being performed by several elements, or any combination of the above. 
     Moreover, it will also be understood that, although the terms first, second, etc. are, in some instances, used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first contact could be termed a second contact, and, similarly, a second contact could be termed a first contact, without departing from the scope of the various described embodiments. The first contact and the second contact are both contacts, but they are not the same contact. 
     The terminology used in the description of the various described embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various described embodiments and the appended claims, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     As used herein, the term “if” is, optionally, construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” is, optionally, construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context. 
     The systems, apparatuses, devices, and methods disclosed herein are described in detail by way of examples and with reference to the figures. The examples discussed herein are examples only and are provided to assist in the explanation of the apparatuses, devices, systems, and methods described herein. None of the features or components shown in the drawings or discussed below should be taken as mandatory for any specific implementation of any of these the apparatuses, devices, systems or methods unless specifically designated as mandatory. For ease of reading and clarity, certain components, modules, or methods may be described solely in connection with a specific figure. In this disclosure, any identification of specific techniques, arrangements, etc. are either related to a specific example presented or are merely a general description of such a technique, arrangement, etc. Identifications of specific details or examples are not intended to be, and should not be, construed as mandatory or limiting unless specifically designated as such. Any failure to specifically describe a combination or sub-combination of components should not be understood as an indication that any combination or sub-combination is not possible. It will be appreciated that modifications to disclosed and described examples, arrangements, configurations, components, elements, apparatuses, devices, systems, methods, etc. can be made and may be desired for a specific application. Also, for any methods described, regardless of whether the method is described in conjunction with a flow diagram, it should be understood that unless otherwise specified or required by context, any explicit or implicit ordering of steps performed in the execution of a method does not imply that those steps must be performed in the order presented but instead may be performed in a different order or in parallel. 
     Throughout this disclosure, references to components or modules generally refer to items that logically can be grouped together to perform a function or group of related functions. Like reference numerals are generally intended to refer to the same or similar components. Components and modules can be implemented in software, hardware, or a combination of software and hardware. The term “software” is used expansively to include not only executable code, for example machine-executable or machine-interpretable instructions, but also data structures, data stores and computing instructions stored in any suitable electronic format, including firmware, and embedded software. The terms “information” and “data” are used expansively and includes a wide variety of electronic information, including executable code; content such as text, video data, and audio data, among others; and various codes or flags. The terms “information,” “data,” and “content” are sometimes used interchangeably when permitted by context. 
     The hardware used to implement the various illustrative logics, logical blocks, modules, and circuits described in connection with the aspects disclosed herein can include a general purpose processor, a digital signal processor (DSP), a special-purpose processor such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA), a programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor can be a microprocessor, but, in the alternative, the processor can be any processor, controller, microcontroller, or state machine. A processor can also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Alternatively, or in addition, some steps or methods can be performed by circuitry that is specific to a given function. 
     In one or more example embodiments, the functions described herein can be implemented by special-purpose hardware or a combination of hardware programmed by firmware or other software. In implementations relying on firmware or other software, the functions can be performed as a result of execution of one or more instructions stored on one or more non-transitory computer-readable media and/or one or more non-transitory processor-readable media. These instructions can be embodied by one or more processor-executable software modules that reside on the one or more non-transitory computer-readable or processor-readable storage media. Non-transitory computer-readable or processor-readable storage media can in this regard comprise any storage media that can be accessed by a computer or a processor. By way of example but not limitation, such non-transitory computer-readable or processor-readable media can include random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), FLASH memory, disk storage, magnetic storage devices, or the like. Disk storage, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Blu-ray Disc™, or other storage devices that store data magnetically or optically with lasers. Combinations of the above types of media are also included within the scope of the terms non-transitory computer-readable and processor-readable media. Additionally, any combination of instructions stored on the one or more non-transitory processor-readable or computer-readable media can be referred to herein as a computer program product. 
     Many modifications and other embodiments of the inventions set forth herein will come to mind to one skilled in the art to which these inventions pertain having the benefit of teachings presented in the foregoing descriptions and the associated drawings. Although the figures only show certain components of the apparatus and systems described herein, it is understood that various other components can be used in conjunction with the supply management system. Therefore, it is to be understood that the inventions are not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Moreover, the steps in the method described above can not necessarily occur in the order depicted in the accompanying diagrams, and in some cases one or more of the steps depicted can occur substantially simultaneously, or additional steps can be involved. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation. 
     It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.