Abstract: A system maintains a knowledge layout to support the building of event response recommendations. Meta-graph patterns may be used to determine semantic relatedness between events and actions in response. Event-action node pairs are then constructed.

Abstract: There has been exponential growth in the capture and retention of immense quantities of information in a globally distributed manner. A closed-loop unified metadata architecture includes a universal metadata repository and implements data quality and data lineage analyses. The architecture solves significant technical challenges to provide a meaningful, consistent and normalized view of the metadata that describes the information, as well as to determine data lineage and meaningful data quality metrics.

Abstract: A method and system for intelligently generating Bill of Materials (BoM) and tracking components of a build site. The method and system include querying a knowledge graph for a collection of historical bill of materials (BoMs), automatically generating a digital twin of a built site based on a selected group of completed build site, and intelligently generating the BoM for the digital twin based on BoMs of the selected group of completed built site.

Abstract: A method and system for intelligently generating Bill of Materials (BoM) and tracking components of a build site. The method and system include querying a knowledge graph for a collection of historical bill of materials (BoMs), automatically generating a digital twin of a built site based on a selected group of completed build site, and intelligently generating the BoM for the digital twin based on BoMs of the selected group of completed built site.

Abstract: A system maintains a knowledge layout to support the building of event response recommendations. Meta-graph patterns may be used to determine semantic relatedness between events and actions in response. Event-action node pairs are then constructed.

Abstract: Methods and systems for a quantum computing approach to solving challenging, e.g., NP-complete, problems in transportation. One of the methods includes (a) ingesting transportation-related data into a graph structure, the transportation-related data being associated with a transportation system; (b) identifying a transportation metric associated with the transportation system; (c) identifying at least one attribute associated with the transportation-related data, where the transportation metric is based at least in part on the attribute; (d) using a quantum computer to derive an operational parameter for the attribute that improves the transportation metric; and (e) applying the operational parameter to the operation of the transportation system.

Abstract: Methods and systems for a quantum computing approach to solving challenging, e.g., NP-complete, problems in transportation. One of the methods includes (a) ingesting transportation-related data into a graph structure, the transportation-related data being associated with a transportation system; (b) identifying a transportation metric associated with the transportation system; (c) identifying at least one attribute associated with the transportation-related data, where the transportation metric is based at least in part on the attribute; (d) using a quantum computer to derive an operational parameter for the attribute that improves the transportation metric; and (e) applying the operational parameter to the operation of the transportation system.

Abstract: Methods and systems for a quantum computing approach to solving challenging, e.g., NP-complete, problems in transportation. One of the methods includes (a) ingesting transportation-related data into a graph structure, the transportation-related data being associated with a transportation system; (b) identifying a transportation metric associated with the transportation system; (c) identifying at least one attribute associated with the transportation-related data, where the transportation metric is based at least in part on the attribute; (d) using a quantum computer to derive an operational parameter for the attribute that improves the transportation metric; and (e) applying the operational parameter to the operation of the transportation system.

Abstract: According to an example, data acceleration may include receiving indications of levels of capabilities respectively needed for data movement, data processing, and data interactivity, and/or operational parameters associated with the data movement, the data processing, and the data interactivity. Data acceleration may further include determining, based on an analysis of the received indications and/or the operational parameters, specifications for the data movement to include streaming and/or batch, the data processing to include a big data platform, complex event processing, and/or an appliance, and the data interactivity to include an in-memory database (IMDB) and/or a distributed cache.

Abstract: Methods and systems for a quantum computing approach to solving challenging, e.g., NP-complete, problems in transportation. One of the methods includes (a) ingesting transportation-related data into a graph structure, the transportation-related data being associated with a transportation system; (b) identifying a transportation metric associated with the transportation system; (c) identifying at least one attribute associated with the transportation-related data, where the transportation metric is based at least in part on the attribute; (d) using a quantum computer to derive an operational parameter for the attribute that improves the transportation metric; and (e) applying the operational parameter to the operation of the transportation system.

Abstract: There has been exponential growth in the capture and retention of immense quantities of information in a globally distributed manner. A closed-loop unified metadata architecture includes a universal metadata repository and implements data quality and data lineage analyses. The architecture solves significant technical challenges to provide a meaningful, consistent and normalized view of the metadata that describes the information, as well as to determine data lineage and meaningful data quality metrics.

Abstract: According to an example, data acceleration may include receiving indications of levels of capabilities respectively needed for data movement, data processing, and data interactivity, and/or operational parameters associated with the data movement, the data processing, and the data interactivity. Data acceleration may further include determining, based on an analysis of the received indications and/or the operational parameters, specifications for the data movement to include streaming and/or batch, the data processing to include a big data platform, complex event processing, and/or an appliance, and the data interactivity to include an in-memory database (IMDB) and/or a distributed cache.

Abstract: The distributed metering and monitoring service (DMMS) system provides a way to gather and maintain metrics data which remains distributed, until requested. The DMMS system uses messaging queues to scale the number of servers that may be monitored and metered to a hyperscale of greater than 10,000 servers. The DMMS system determines how many servers (nodes) to assign to a cluster, and uses a metric aggregator to collect and store metrics data for the nodes. The DMMS system creates message queues for the instances, injects instance identifiers into the cluster state data and metrics data, listens for request messages for metering information for instances, retrieves the metrics data for users identified by the instance identifiers stored locally at the nodes, and calculates the metering information for the instance.

Abstract: The distributed metering and monitoring service (DMMS) system provides a way to gather and maintain metrics data which remains distributed, until requested. The DMMS system uses messaging queues to scale the number of servers that may be monitored and metered to a hyperscale of greater than 10,000 servers. The DMMS system determines how many servers (nodes) to assign to a cluster, and uses a metric aggregator to collect and store metrics data for the nodes. The DMMS system creates message queues for the instances, injects instance identifiers into the cluster state data and metrics data, listens for request messages for metering information for instances, retrieves the metrics data for users identified by the instance identifiers stored locally at the nodes, and calculates the metering information for the instance.

Abstract: The distributed metering and monitoring service (DMMS) system provides a way to gather and maintain metrics data which remains distributed, until requested. The DMMS system uses messaging queues to scale the number of servers that may be monitored and metered to a hyperscale of greater than 10,000 servers. The DMMS system determines how many servers (nodes) to assign to a cluster, and uses a metric aggregator to collect and store metrics data for the nodes. The DMMS system creates message queues for the instances, injects instance identifiers into the cluster state data and metrics data, listens for request messages for metering information for instances, retrieves the metrics data for users identified by the instance identifiers stored locally at the nodes, and calculates the metering information for the instance.

Abstract: The distributed metering and monitoring service (DMMS) system provides a way to gather and maintain metrics data which remains distributed, until requested. The DMMS system uses messaging queues to scale the number of servers that may be monitored and metered to a hyperscale of greater than 10,000 servers. The DMMS system determines how many servers (nodes) to assign to a cluster, and uses a metric aggregator to collect and store metrics data for the nodes. The DMMS system creates message queues for the instances, injects instance identifiers into the cluster state data and metrics data, listens for request messages for metering information for instances, retrieves the metrics data for users identified by the instance identifiers stored locally at the nodes, and calculates the metering information for the instance.