Abstract: A knowledge data management system (KDMS) implements data refinement orchestration, resolution, and refinement to provide a reusable and generic solution to ingest and link data via relationships and properties when constructing a knowledge graph. The KDMS thus operates to break down existing data storage silos by normalizing and integrating data with a uniform semantic schema, which results in more accurate and faster knowledge graph construction.

Abstract: A piping and instrumentation planning and maintenance system includes an input/output (I/O) interface for receiving a target piping and instrumentation diagram (PID) from a document source system; a processor in communication with the I/O interface; and non-transitory computer readable media in communication with the processor. The non-transitory computer readable media store instruction code, which when executed by the processor, causes the processor to classify entities and properties thereof within the target PID. The entities include one or more assets and interconnections therebetween specified in the PID. The processor compares the classified entities to a knowledge base that represents relationships between a plurality of assets and interconnections between the assets. The processor then determines, based on the comparison, whether the assets in the target PID are interconnected correctly.

Abstract: A data generation stack executing on data processing circuitry may generate substitute data for provision in place of data sample from sensor circuitry. A configuration layer of the data generation stack may store a configuration file. A data ingestion and generation layer of the data generation stack may access the configuration file to determine static relationships among different sensors within the system. The data ingestion and generation layer may further determine dynamic relationships among the sensors. Based on a hybrid relationship that accounts for the static and dynamic relationships, the data ingestion and generation layer may generate substitute data for a sensor based on sampled data from another sensor. A data export layer of the data generation stack may access the generated substitute data for output.

Abstract: Knowledge graph systems are disclosed for implementing multiple approaches, including stand alone or combined approaches, for knowledge graph pruning. The approaches are based on graph sampling work such as, for example, information gain theory. The approaches are applied by a knowledge graph system to perform schema pruning, automatic graph pruning, and query correlation for improving query performance.

Abstract: Knowledge graph systems are disclosed for implementing multiple approaches, including stand alone or combined approaches, for knowledge graph pruning. The approaches are based on graph sampling work such as, for example, information gain theory. The approaches are applied by a knowledge graph system to perform schema pruning, automatic graph pruning, and query correlation for improving query performance.

Abstract: A knowledge graph virtualization system (KGVS) implements data virtualization techniques using semantic knowledge when parsing a knowledge graph to return results to a query. The data virtualization using semantic knowledge enable end users and applications to describe desired output without detailed knowledge of underlying data storage, schema, and relationships.

Abstract: A piping and instrumentation planning and maintenance system includes an input/output (I/O) interface for receiving a target piping and instrumentation diagram (PID) from a document source system; a processor in communication with the I/O interface; and non-transitory computer readable media in communication with the processor. The non-transitory computer readable media store instruction code, which when executed by the processor, causes the processor to classify entities and properties thereof within the target PID. The entities include one or more assets and interconnections therebetween specified in the PID. The processor compares the classified entities to a knowledge base that represents relationships between a plurality of assets and interconnections between the assets. The processor then determines, based on the comparison, whether the assets in the target PID are interconnected correctly.

Abstract: A knowledge data management system (KDMS) implements data refinement orchestration, resolution, and refinement to provide a reusable and generic solution to ingest and link data via relationships and properties when constructing a knowledge graph. The KDMS thus operates to break down existing data storage silos by normalizing and integrating data with a uniform semantic schema, which results in more accurate and faster knowledge graph construction.

Abstract: A method, computer-readable medium, and system for provisioning computing resources across multiple cloud providers and/or data centers are disclosed. A graphical user interface is used to select a plurality of computing resources and at least one cloud provider and/or at least one data center for providing the plurality of computing resources. Scripts associated with the at least one cloud provider and/or at least one data center are accessed, where each script is capable of automatically setting up a computing resource on an associated cloud provider or associated data center. The scripts are then used to automatically allocate and/or configure the computing resources on the at least one cloud provider and/or at least one data center. As such, computing resources can be automatically provisioned using a generic graphical user interface and without a user having skills or credentials specific to each cloud provider and/or data center.

Abstract: A method, computer-readable medium, and system for monitoring usage of computing resources provisioned across multiple cloud providers and/or data centers are disclosed. Events associated with usage of a plurality of computing resources may be accessed, where the plurality of computing resources may implement a virtual machine, a plurality of virtual machines of a cloud computing environment, etc. The events may be associated with a start, a stop, a status change, etc., of the plurality of computing resources. The events may be used to generate usage data for the plurality of computing resources. The usage data may include historical data associated with previous usage of the plurality of computing resources. Additionally, the usage data may be displayed using a graphical user interface, thereby enabling monitoring and/or tracking of usage of computing resources provisioned across at least one cloud provider and/or at least one data center.

Abstract: A piping and instrumentation planning and maintenance system includes an input/output (I/O) interface for receiving a target piping and instrumentation diagram (PID) from a document source system; a processor in communication with the I/O interface; and non-transitory computer readable media in communication with the processor. The non-transitory computer readable media store instruction code, which when executed by the processor, causes the processor to classify entities and properties thereof within the target PID. The entities include one or more assets and interconnections therebetween specified in the PID. The processor compares the classified entities to a knowledge base that represents relationships between a plurality of assets and interconnections between the assets. The processor then determines, based on the comparison, whether the assets in the target PID are interconnected correctly.

Abstract: A method, computer-readable medium, and system for provisioning computing resources across multiple cloud providers and/or data centers are disclosed. A graphical user interface is used to select a plurality of computing resources and at least one cloud provider and/or at least one data center for providing the plurality of computing resources. Scripts associated with the at least one cloud provider and/or at least one data center are accessed, where each script is capable of automatically setting up a computing resource on an associated cloud provider or associated data center. The scripts are then used to automatically allocate and/or configure the computing resources on the at least one cloud provider and/or at least one data center. As such, computing resources can be automatically provisioned using a generic graphical user interface and without a user having skills or credentials specific to each cloud provider and/or data center.

Abstract: An interaction tracking system tracks user interactions with an application. The tracked interactions may be stored in a data structure along with information describing an application state of the tracked application resulting from the tracked interactions. The interaction tracking system stores the data structure in a database for subsequent search and retrieval, which allows different users to activate the stored data structure and initiate the application in application state described in the data structure. The interaction tracking system also transmits the data structure to other communication devices, where the data structure is activated on the receiving communication device.

Abstract: A system maintains, generates, and manages infrastructure layouts. The infrastructure layouts include structural relationships for the handling of received sensor data. The infrastructure layouts may be traversed to determine datastore options for the sensor data and contextual information that may be used to enrich the received sensor data.

Abstract: A query rewriting processor (processor) analyzes database semantic models (e.g., RDF knowledge graphs) that capture the interconnections (e.g., foreign and primary key links to other tables) present in a relational database. The processor generates an enriched model query given an initial model query (e.g., a SPARQL query) against the semantic model. The processor generates the enriched model query and translates the enriched model query into a relational database query (e.g., an SQL query). The processor may then pass the relational database query to another system or process (e.g., a data virtualization layer) for execution against the individual relational databases. In this manner, the processor automatically translates queries for information about the relational database structure to a corresponding or matching query for data from the relational database structure.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating multiple distinct test plans using a computing system. The system merges the each of the test plans to produce a first test plan and obtains performance data about automated execution of the first test plan by a robotics system. The system generates learned inferences for determining predictions about execution of the first test plan based on analysis of the performance data and adjusts the first test plan based on the learned inferences. The system generates a second test plan for execution by the computing system based on adjustments to the first test plan and performs the second test plan to test multiple devices using the robotics system.

Abstract: Scalable architectures provide resiliency and redundancy and are suitable for cloud deployment. The architectures support extreme data throughput requirements. In one implementation, the architectures provide a serving layer and an extremely high speed processing lane. With these and other features, the architectures support complex analytics, visualization, rule engines, and centralized pipeline configuration.

Abstract: A knowledge graph virtualization system (KGVS) implements data virtualization techniques using semantic knowledge when parsing a knowledge graph to return results to a query. The data virtualization using semantic knowledge enable end users and applications to describe desired output without detailed knowledge of underlying data storage, schema, and relationships.

Abstract: A development platform system is disclosed that provides a development platform configured to create new and modified web-based applications. The development platform may be configured to search for and select a design layout template from available design layout templates stored on a database library. The development platform may further be configured to search for and select a data visualization template from available data visualization templates stored on a database library. The development platform may further be configured to generate a data visualization modification interface and accept a client device customization input to modify attributes of a data visualization. Templates that are modified by the development platform may be stored for subsequent look-up on a database library according to metadata describing attributes of the templates.

Abstract: A knowledge enabled data management system ingests data and stores the data as an instance in a knowledge graph according to a domain specific ontology. The instance includes stored relationships of the entities in the instance. A query regarding the data may be parsed to derive a first query entity that is used to search the knowledge graph for a first graph entity corresponding to the first query entity. Results may be returned including a first identification of the first graph entity, a second identification of at least a second graph entity related to the first graph entity stored within the knowledge graph, and additional data corresponding to the first graph entity and to the second graph entity. The additional data may include a probabilistic measure of the relationship of the first graph entity to the second graph entity.