Abstract: According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising receiving a node group at an integration module, wherein the node group includes one or more requests for internal data and external data, wherein internal data is data stored in an internal datastore and external data is data stored outside of the internal data store; identifying in a configuration data, one or more meta-data nodes from the node group, wherein each meta-data node indicates a request for external data; retrieving the internal data via execution of an internal data query; determining an order of execution for the one or more meta-data nodes; executing a first meta-data node based on the determined order to generate a first result; ingesting the first result into a semantic datastore; and executing a query to generate a final result, wherein the query includes the retrieved internal data and the ingested first result. Numerous other aspects are provided.

Abstract: According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising receiving a node group at an integration module, wherein the node group includes one or more requests for internal data and external data, wherein internal data is data stored in an internal datastore and external data is data stored outside of the internal data store; identifying in a configuration data, one or more meta-data nodes from the node group, wherein each meta-data node indicates a request for external data; retrieving the internal data via execution of an internal data query; determining an order of execution for the one or more meta-data nodes; executing a first meta-data node based on the determined order to generate a first result; ingesting the first result into a semantic datastore; and executing a query to generate a final result, wherein the query includes the retrieved internal data and the ingested first result. Numerous other aspects are provided.

Abstract: A system for querying a federated data store includes a metadata knowledge graph describing the contents and relationships among one or more underlying data stores, an interactive user interface receiving requests from a data consumer, a predefined constrainable query (‘nodegroup’) store containing predefined constrainable queries that define data subsets of interest across one or more of the underlying data repositories, a knowledge-driven querying layer generating and executing queries against the federated data store and merging responsive results, a scalable analytic execution layer receiving the search results from the federated data store and applying machine learning/artificial intelligence techniques to analyze the results, and a user interface presenting visualizations of raw or analyzed results to the consumer. A method and a non-transitory computer-readable medium are also disclosed.

Abstract: Some embodiments are directed to systems for authoring predictive models. An embodiment includes a computer system implementing a development environment for generating predictive models. The predictive model authoring tool is configured to perform a modeling operation based on one or more user inputs provided to interface controls of the predictive model authoring tool, determine a modeling context for the modeling operation, log the one or more user inputs, generate a predictive model based on one or more model parameters defined during the modeling operation, link the predictive model to an asset, such that one or more sets of data received from the asset are provided to the predictive model during execution of the predictive model, cause the predictive model to be executed such that the predictive model receives data from the asset, and provide the modeling context, the one or more user inputs, and the one or more model parameters.

Abstract: A system to generate and run federated queries against a plurality of data stores storing disparate data types, the system including a user interface receiving query details from a data consumer, a metadata knowledge graph containing metadata for links and relationships of the data stores, a knowledge-driven querying layer accessing the graph and selecting predefined constrainable queries from a nodegroup store and applying the metadata links/relationships to the predefined constrainable queries to assemble subqueries, a query and analysis platform providing the subqueries to some of the data stores for execution, a scalable analytic execution layer receiving and aggregating search results from the data stores into a merged search result and/or obtaining analytic results by applying machine learning and artificial intelligence techniques to the distributed data, the user interface presenting visualizations generated from the merged search results, and/or the analytic results.

Abstract: According to some embodiments, system and methods for building a model are provided, comprising a display; a memory storing processor-executable process steps; and a processor to execute the processor-executable process steps to cause the system to: present a user interface on a display, the user interface including one or more user-entry fields to build a model, user-entry fields is associated with a selection of big data or small data for use with the model; receive at least one data source in a user-entry field associated with the model; determine if data in the data source includes big data or small data; and in response to the determination of big data or small data in the data source, execute the model with data from the data source in a big data or small data execution environment. Numerous other aspects are provided.

Abstract: A system for querying a federated data store includes a metadata knowledge graph describing the contents and relationships among one or more underlying data stores, an interactive user interface receiving requests from a data consumer, a predefined constrainable query (‘nodegroup’) store containing predefined constrainable queries that define data subsets of interest across one or more of the underlying data repositories, a knowledge-driven querying layer generating and executing queries against the federated data store and merging responsive results, a scalable analytic execution layer receiving the search results from the federated data store and applying machine learning/artificial intelligence techniques to analyze the results, and a user interface presenting visualizations of raw or analyzed results to the consumer. A method and a non-transitory computer-readable medium are also disclosed.

Abstract: A system to query a federated store containing disparate data types and stores, the system including a UI or API to specify query details, a metadata knowledge graph with metadata describing the contents of the data stores, the relationships among them, and how to programmatically query the data stores, a predefined constrainable query (‘nodegroup’) store containing nodegroups providing a template to search the data stores, the querying layer including services and libraries to process nodegroups and generate a set of queries, a query and analysis platform providing the set of queries to at least one data store for execution at the federated stores and return a result, a scalable analytic execution layer applying machine learning/artificial intelligence techniques to analyze the query results and presenting data analysis result visualizations. A method and a non-transitory medium are also disclosed.

Abstract: A system to generate and run federated queries against a plurality of data stores storing disparate data types, the system including a user interface receiving query details from a data consumer, a metadata knowledge graph containing metadata for links and relationships of the data stores, a knowledge-driven querying layer accessing the graph and selecting predefined constrainable queries from a nodegroup store and applying the metadata links/relationships to the predefined constrainable queries to assemble subqueries, a query and analysis platform providing the subqueries to some of the data stores for execution, a scalable analytic execution layer receiving and aggregating search results from the data stores into a merged search result and/or obtaining analytic results by applying machine learning and artificial intelligence techniques to the distributed data, the user interface presenting visualizations generated from the merged search results, and/or the analytic results.

Abstract: According to some embodiments, a system and method are provided to extract relationships from unstructured text documents. The method comprises receiving a training set of sentences that comprise labeled objects and subjects for creating an initial relationship model. A set of unlabeled sentences may be received. Objects and subjects from the set of unlabeled sentences are determined based on the initial model and the determined objects and subjects from the set of unlabeled sentences are displayed to a user for feedback and approval. An indication of whether the determined objects and subjects from the set of unlabeled sentences are correct is received and the initial relationship model is updated based on the received indication.

Abstract: According to some embodiments, a system and method are provided to extract relationships from unstructured text documents. The method comprises receiving a training set of sentences that comprise labeled objects and subjects for creating an initial relationship model. A set of unlabeled sentences may be received. Objects and subjects from the set of unlabeled sentences are determined based on the initial model and the determined objects and subjects from the set of unlabeled sentences are displayed to a user for feedback and approval. An indication of whether the determined objects and subjects from the set of unlabeled sentences are correct is received and the initial relationship model is updated based on the received indication.

Abstract: According to some embodiments, system and methods for representing nodes and data flows in a network are provided, comprising providing a hierarchical taxonomy for one or more concepts; providing one or more hierarchical taxonomies for one or more boundary types, wherein one or more values from each boundary type are combined to form a definition of a boundary; and associating the at least one concept and at least one boundary with a transmission of data between a first node and a second node, wherein the transmitted data is a data flow. Numerous other aspects are provided.

Abstract: According to some embodiments, system and methods are provided, comprising a collaboration analytic module; a display; a memory storing processor-executable process steps; and a collaboration analytic processor coupled to the memory, and in communication with the collaboration analytic module and operative to execute the processor-executable process steps to cause the system to: initiate a collaboration session; receive input at the collaboration session, wherein the collaboration session is integrated with a source of the received input; generate a visualization based on the received input; receive selection from a user of at least one collaborator to participate in the collaboration session; present the visualization on the display, wherein the visualization is viewable by the user and at least one collaborator; receive a manipulation of the visualization; record the manipulation of the visualization; and associate the recorded manipulation with a manipulation source. Numerous other aspects are provided.

Abstract: A system and method for searching for and finding data across industrial time-series data is disclosed. A computer system receives a search query from a client system. The computer system accesses a database including a plurality of stored time-series data sets. For each stored time-series data set, the computer system determines whether the stored time-series data set includes one or more sections that match the received search query. In accordance with a determination that two or more of stored time-series data sets include at least one section that matches the received search query, the computer system determines whether the matching sections in each stored time-series data set have overlapping time periods. In accordance with a determination that the matching sections in each time-series data set have overlapping time periods, the computer system identifies a particular event that occurred during the overlapping time periods.

Abstract: A method, system, and non-transitory computer-readable medium, the method including receiving notifications from a plurality of agents, the notifications being associated with the plurality of agents sensing aspects of an environment; determining, based at least in part on the received notifications from the plurality of agents, a situational model of the environment from the notifications; determining a status of the environment based on the situational model; and reporting the status of the environment to at least one of the plurality of agents.

Abstract: According to some embodiments, system and methods for representing nodes and data flows in a network are provided, comprising providing a hierarchical taxonomy for one or more concepts; providing one or more hierarchical taxonomies for one or more boundary types, wherein one or more values from each boundary type are combined to form a definition of a boundary; and associating the at least one concept and at least one boundary with a transmission of data between a first node and a second node, wherein the transmitted data is a data flow. Numerous other aspects are provided.

Abstract: According to some embodiments, system and methods for building a model are provided, comprising a display; a memory storing processor-executable process steps; and a processor to execute the processor-executable process steps to cause the system to: present a user interface on a display, the user interface including one or more user-entry fields to build a model, user-entry fields is associated with a selection of big data or small data for use with the model; receive at least one data source in a user-entry field associated with the model; determine if data in the data source includes big data or small data; and in response to the determination of big data or small data in the data source, execute the model with data from the data source in a big data or small data execution environment. Numerous other aspects are provided.

Abstract: Examples relate to systems for authoring and executing predictive models. A computer system includes a model development context analyzer configured to store a set of derived modeling knowledge generated at least in part from a plurality of modeling operations performed using at least a first predictive model authoring tool. The system is configured to, receive a modeling context indicating at least a modeling operation being performed, determine, from the modeling context, at least one element of an ontology, the ontology defining at least one attribute of a plurality of modeling operations, query the set of derived modeling knowledge using the at least one element of the ontology to identify at least one record of the set of derived modeling knowledge associated with the at least one element of the ontology, identify at least one suggested model parameter associated with the modeling context, and provide the at least one suggested model parameter.

Abstract: Some embodiments are directed to systems for authoring predictive models. An embodiment includes a computer system implementing a development environment for generating predictive models. The predictive model authoring tool is configured to perform a modeling operation based on one or more user inputs provided to interface controls of the predictive model authoring tool, determine a modeling context for the modeling operation, log the one or more user inputs, generate a predictive model based on one or more model parameters defined during the modeling operation, link the predictive model to an asset, such that one or more sets of data received from the asset are provided to the predictive model during execution of the predictive model, cause the predictive model to be executed such that the predictive model receives data from the asset, and provide the modeling context, the one or more user inputs, and the one or more model parameters.

Abstract: A system includes a processor and a non-transitory computer-readable medium. The non-transitory computer-readable medium comprises instructions executable by the processor to cause the system to perform a method. The method comprises receiving a first job to execute and executing the first job. A plurality of data associated with the first job is determined. The plurality of data comprises data associated with (i) a second job executed immediately prior to the first job, (ii) a third job executed immediately after the first job, (iii) a determination of whether the first job failed or executed successfully and (iv) a type of data associated with the first job. The determined plurality of data is stored.