General Electric Company

Genworth Financial, Inc.

Ge Intelligent Platforms Inc.

Kareem Sherif Aggour

John Alan Interrante

Piero Patrone Bonissone

Christina Ann LaComb

Jenny Marie Weisenberg Williams

Vijay Shiv Kumar

Paul Edward Cuddihy

Rajesh Venkat Subbu

Sunil Mathur

Jerry Lin

Brian Scott Courtney

Christopher Thomas McConnell

Quinn Snell

Barbara Jean Vivier

Justin Despenza McHugh

Weizhong Yan

Naresh Sundaram Iyer

Abha Moitra

Richard Paul Messmer

Steven Matt Gustafson

Anthony Joseph Vinciquerra

Mark Mitchell Kornfein

Varish Vyankatesh Mulwad

Marc Pavese

Angela Patterson

Dan Yang

Anindya Chakraborty

Janet Arlie Barnett

Luis Babaji Ng Tari

Charles Burton Theurer

Achalesh Kumar Pandey

John Michael Lizzi

Anil Varma

Gerald Bowden Wise

Helena Goldfarb

Osman Rifki Oksoy

Bassel Omari Williams

Ibrahim Gokcen

Aisha Yousuf

Christina Ann Leber

Jenny Weisenberg Williams

Alfredo Gabaldon Royval

Ward Linnscott Bowman

Jenny Marie Williams

Paul Cuddihy

Christopher Thomas Mcconnell

Christina Ann Lacomb

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.

System and method for performing semantically-informed federated queries across a polystore

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.

Knowledge-driven federated big data query and analytics platform

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.

Methods and systems for capturing analytic model authoring knowledge

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. A system and a non-transitory computer-readable medium are also disclosed.

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.

Multimodal, small and big data, machine tearing systems and processes

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.

Systems and methods for learning to extract relations from text via user feedback

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.

Mehtod and system for multi-modal lineage tracing and impact assessment in a concept lineage data flow network

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.

Searching for and finding data across industrial time series data

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.

System and method for the creation and utilization of multi-agent dynamic situational awareness models

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.

Automation of workflow creation and failure recovery

Methods and systems for optimizing the configuration and parameters of a workflow using an evolutionary approach augmented with intelligent learning capabilities using a Big Data infrastructure. In an embodiment, a Big Data infrastructure receives workflow input parameters, an objective function, a pool of initial configuration parameters, and completion criteria from a client computer, and then runs multiple instances of a workflow based on the pool of initial configuration parameters resulting in corresponding output results. The process includes storing the workflow input parameters and the corresponding output results, modeling the relationship between changes in the workflow input parameters and the corresponding output results, determining that optimal output results have been achieved, and then transmitting the optimal output and the input-output variable relationships results to the client computer.

Methods and systems for intelligent evolutionary optimization of workflows using big data infrastructure

A system for storing time series data includes an ingester that prepares metadata indices associated with blocks of incoming time series data and stores the blocks of data in a time series database and the indices in a separate index database. The time series database distributes storage of the data blocks among multiple data nodes. A query layer receives queries and uses the index database to determine which data blocks are needed to process the query, and then requests only those data blocks from the time series database. Processing of the query is performed within the time series database only on those data nodes that contain relevant data, and partial results are passed to an output layer for formation into a final query result.

System for storage, querying, and analysis of time series data

A method for performing queries on a distributed time series data storage system is presented. The time series data storage system has a time series database that stores data blocks containing time stamped data across a plurality of computing devices. The system also includes an index database that stores an index associated with the time stamped data in each data block. The method includes the steps of sending a query, requesting indices, returning the indices, preparing a sub-query, forwarding the sub-query to an evaluator, evaluating the sub-query, performing a logical operation on each sub-query's result, receiving the sub-results at an output handler, and combining the sub-results.

Method for storage, querying, and analysis of time series data

A service for storing time series data provides a data pipe for receiving time series data, a query pipe for making requests to the service, and a result pipe for receiving output from the service. Data sent to the query pipe is processed by an ingester that prepares metadata indices associated with blocks of incoming time series data and stores the blocks of data in a time series database and the indices in a separate index database. A query layer receives queries from the query pipe and uses the index database to determine which data blocks are needed to process the query, and then requests only those data blocks from the time series database. Processing of the query is performed within the time series database only on those data nodes that contain relevant data, and partial results are passed to an output layer for formation into a final query result which is sent out by the results pipe.

System and method for storage, querying, and analysis service for time series data

Growing community of inventors

Niskayuna, NY, United States of America

Kareem Sherif Aggour