Abstract: An example operation may include one or more of a computer storing a first set of received data analytics in a blockchain, the first set associated with a subject matter. The operation further comprises the one or more computer storing a second set of received data analytics in the blockchain, the second set associated with the subject matter. The operation further comprises the one or more computer deriving and storing in the blockchain a first set of metrics based on analysis of the first set of analytics processed with the second set of analytics. The operation further comprises the one or more computer storing a third set of received data analytics in the blockchain, the third set associated with the subject matter, and deriving and storing a second set of metrics based on analysis of the first set of metrics processed with the third set of data analytics.

Abstract: Operational data in a distributed processing system is managed by monitoring a workload of the system to establish a current assessment of operational data movement between data sources and data targets, receiving historical information on previous data movement including previous instances of movement resulting in a compromise of one or more quality-of-service criteria, determining from the current assessment and historical information that upcoming operational data actions will not meet a particular quality-of-service criterion, and responsively applying a data management optimization infrastructure (data backplane services) adapted to advance the particular quality-of-service criterion according to definitions for the data sources and data targets. The operational outcome is predicted using a cognitive system trained with historical information including historical operational factors correlated with historical operational outcomes relative to the quality-of-service criteria.

Abstract: A system, computer program product, and method are presented for determining illegitimate business entities, and, more specifically, to distinguishing between legitimate business entities and illegitimate business entities. The method includes identifying a target entity using known attributes of the target entity and collecting, from one or more external sources, additional attributes of the target entity. The method also includes injecting the known attributes and the additional attributes into one or more models including at least one of one or more machine learning models and one or more statistical models. The method further includes generating, through the one or more machine learning models, one or more scores that indicate a probability that the target entity is an illegitimate business.

Abstract: Operational data in a distributed processing system is managed by monitoring a workload of the system to establish a current assessment of operational data movement between data sources and data targets, receiving historical information on previous data movement including previous instances of movement resulting in a compromise of one or more quality-of-service criteria, determining from the current assessment and historical information that upcoming operational data actions will not meet a particular quality-of-service criterion, and responsively applying a data management optimization infrastructure (data backplane services) adapted to advance the particular quality-of-service criterion according to definitions for the data sources and data targets. The operational outcome is predicted using a cognitive system trained with historical information including historical operational factors correlated with historical operational outcomes relative to the quality-of-service criteria.

Abstract: An example operation may include one or more of a computer deriving a first set of metrics from processing a first and second set of data analytics, the sets associated with a subject matter. The operation further comprises the one or more computer deriving a second set of metrics from processing a third and fourth sets of data analytics, the third and fourth sets associated with the subject matter. The operation further comprises the one or more computer publishing the first and second set of metrics. The operation further comprises the one or more computer receiving a first plurality of requests for processing of analytics using the first set of metrics. The operation further comprises the one or more computer receiving a second plurality of requests for processing of analytics using the second set of metrics. The operation further comprises the one or more computer maintaining tallies of the requests.

Abstract: A blockchain function may include one or more of receiving a last observed location identifier for a product identified via a product tag associated with the product, determining whether the last observed location identifier was received from a trusted source device, and storing the last observed location identifier in a blockchain with a confirmed status responsive to determining the last observed location identifier was received from the trusted source device.

Abstract: Operational data in a distributed processing system is managed by monitoring a workload of the system to establish a current assessment of operational data movement between data sources and data targets, receiving historical information on previous data movement including previous instances of movement resulting in a compromise of one or more quality-of-service criteria, determining from the current assessment and historical information that upcoming operational data actions will not meet a particular quality-of-service criterion, and responsively applying a data management optimization infrastructure (data backplane services) adapted to advance the particular quality-of-service criterion according to definitions for the data sources and data targets. The operational outcome is predicted using a cognitive system trained with historical information including historical operational factors correlated with historical operational outcomes relative to the quality-of-service criteria.

Abstract: Operational data in a distributed processing system is managed by monitoring a workload of the system to establish a current assessment of operational data movement between data sources and data targets, receiving historical information on previous data movement including previous instances of movement resulting in a compromise of one or more quality-of-service criteria, determining from the current assessment and historical information that upcoming operational data actions will not meet a particular quality-of-service criterion, and responsively applying a data management optimization infrastructure (data backplane services) adapted to advance the particular quality-of-service criterion according to definitions for the data sources and data targets. The operational outcome is predicted using a cognitive system trained with historical information including historical operational factors correlated with historical operational outcomes relative to the quality-of-service criteria.

Abstract: A blockchain of transactions may be referenced for various purposes and may be later accessed by interested parties for ledger verification. One example method of operation may include reading a tag affixed to an asset, transmitting a request to update an asset status of the asset in a blockchain, receiving validation confirmation based on content of the request, and updating the asset status of the asset in the blockchain.

Abstract: An example operation may include one or more of a computer deriving a first set of metrics from processing a first and second set of data analytics, the sets associated with a subject matter. The operation further comprises the one or more computer deriving a second set of metrics from processing a third and fourth sets of data analytics, the third and fourth sets associated with the subject matter. The operation further comprises the one or more computer publishing the first and second set of metrics. The operation further comprises the one or more computer receiving a first plurality of requests for processing of analytics using the first set of metrics. The operation further comprises the one or more computer receiving a second plurality of requests for processing of analytics using the second set of metrics. The operation further comprises the one or more computer maintaining tallies of the requests.

Abstract: An example operation may include one or more of a computer storing a first set of received data analytics in a blockchain, the first set associated with a subject matter. The operation further comprises the one or more computer storing a second set of received data analytics in the blockchain, the second set associated with the subject matter. The operation further comprises the one or more computer deriving and storing in the blockchain a first set of metrics based on analysis of the first set of analytics processed with the second set of analytics. The operation further comprises the one or more computer storing a third set of received data analytics in the blockchain, the third set associated with the subject matter, and deriving and storing a second set of metrics based on analysis of the first set of metrics processed with the third set of data analytics.

Abstract: A blockchain of transactions may be referenced for various purposes and may be later accessed by interested parties for ledger verification. One example method of operation may include reading a tag affixed to an asset, transmitting a request to update an asset status of the asset in a blockchain, receiving validation confirmation based on content of the request, and updating the asset status of the asset in the blockchain.

Abstract: A blockchain of transactions may be referenced for various purposes and may be later accessed by interested parties for ledger verification. One example method of operation may include reading a tag affixed to an asset, transmitting a request to update an asset status of the asset in a blockchain, receiving validation confirmation based on content of the request, and updating the asset status of the asset in the blockchain.

Abstract: A blockchain function may include one or more of receiving a last observed location identifier for a product identified via a product tag associated with the product, determining whether the last observed location identifier was received from a trusted source device, and storing the last observed location identifier in a blockchain with a confirmed status responsive to determining the last observed location identifier was received from the trusted source device.

Abstract: A blockchain of transactions may be referenced for various purposes and may be later accessed by interested parties for ledger verification. One example method of operation may include reading a tag affixed to an asset, transmitting a request to update an asset status of the asset in a blockchain, receiving validation confirmation based on content of the request, and updating the asset status of the asset in the blockchain.