Abstract: An example operation may include one or more of acquiring, by a retailer node, an inventory data from a supplier node over a blockchain network, receiving, by the retailer node, outstanding orders data of the supplier node, generating, by the retailer node, an order distribution policy based on the inventory data and the outstanding orders data, and executing a smart contract to order goods from the supplier node based on the ordering policy.

Abstract: An example operation may include one or more of detecting, by a blockchain node, an asset aggregation or disaggregation event, performing, by the blockchain node, a single-shot update of an asset containment world-state at an ingestion of the asset aggregation or disaggregation event, determining, by the blockchain node, parent-child duration parameters for each instance of an asset parent-child association defined by the asset containment world-state, and executing a linear-time algorithm to calculate supply-chain metrics based on all combinations of the asset aggregation or disaggregation orderings based on the parent-child duration parameters.

Abstract: An example operation may include one or more of acquiring, by a retailer node, an inventory data from a supplier node over a blockchain network, receiving, by the retailer node, outstanding orders data of the supplier node, generating, by the retailer node, an order distribution policy based on the inventory data and the outstanding orders data, and executing a smart contract to order goods from the supplier node based on the ordering policy.

Abstract: Embodiments relate to personalized low latency communications. A method may include receiving a description of content of a message, receiving recipient data corresponding to at least two possible recipients within a population of possible recipients, and selecting a relevant subpopulation of the population. The selecting may include, for each of the at least two possible recipients, ranking a strength of an indirect relationship between the description and the recipient data. The indirect relationship may be based on the description, the recipient data and at least one additional data source. The selecting may also include, for each of the at least two possible recipients, adding a possible recipient to the relevant subpopulation based on the ranking of the indirect relationship associated with the possible recipient. The method may further include initiating a two-way communication channel between a sender of the message and the relevant subpopulation.

Abstract: Embodiments relate to personalized low latency communications. A method may include receiving a description of content of a message, receiving recipient data corresponding to at least two possible recipients within a population of possible recipients, and selecting a relevant subpopulation of the population. The selecting may include, for each of the at least two possible recipients, ranking a strength of an indirect relationship between the description and the recipient data. The indirect relationship may be based on the description, the recipient data and at least one additional data source. The selecting may also include, for each of the at least two possible recipients, adding a possible recipient to the relevant subpopulation based on the ranking of the indirect relationship associated with the possible recipient. The method may further include initiating a two-way communication channel between a sender of the message and the relevant subpopulation.

Abstract: According to embodiments, methods, systems, and computer program products are provided for receiving one or more input compositions comprising one or more materials, assigning a material vector to each material, learning, for each of the input compositions, a composition vector based on the material vectors of the materials that form each composition, assigning predicted rating values having a confidence level to each of the composition vectors, selecting a composition to be rated based on the confidence levels, presenting the selected composition to be rated to a user, receiving a user rating for the composition to be rated; adjusting the predicted rating values and confidence levels of the composition vectors that have not been rated by the user, and generating a predictive model to predict a user's ratings for compositions when confidence levels of each composition vector is above a predetermined threshold value.

Abstract: According to embodiments, methods, systems, and computer program products are provided for receiving one or more input compositions comprising one or more materials, assigning a material vector to each material, learning, for each of the input compositions, a composition vector based on the material vectors of the materials that form each composition, assigning predicted rating values having a confidence level to each of the composition vectors, selecting a composition to be rated based on the confidence levels, presenting the selected composition to be rated to a user, receiving a user rating for the composition to be rated; adjusting the predicted rating values and confidence levels of the composition vectors that have not been rated by the user, and generating a predictive model to predict a user's ratings for compositions when confidence levels of each composition vector is above a predetermined threshold value.

Abstract: A computer system, method, and computer-readable product for providing data for career advice, such as job or education recommendations, from automated review and analysis of career-related data for an individual, which is at least initially obtained from documents, such as resumes and writing samples. For a designated individual, career-related data is obtained from the documents and an initial personality estimate is created for the designated individual based upon, at least, the obtained career-related data. One or more latent factor models for known career-personality matches are then gathered from a database and a questionnaire is provided to the individual to gather further information and augment the personality estimate. The created personality estimate is integrated with the latent factor model(s) to create career advice data.

Abstract: An example operation may include one or more of partitioning a data set from a data provider into a training data set and a test data set, exposing the training data set to a learning service provider while preventing the learning service provider from being able to access the test data set, wherein the preventing comprises encrypting the test data set and storing the encrypted test data set in an immutable ledger, receiving a learning model that is generated by the learning service provider based on the exposed training data set, executing the received learning model using the test data set as input to verify whether the learning model satisfies a predefined performance threshold, and in response to verifying the learning model satisfies the predefined performance threshold, outputting information about the verification to a computing node.

Abstract: An example operation includes one or more of traversing, by a modeling node, a supply-chain downstream from an initial step, detecting, by a modeling node, a multi-organization step, and responsive to the detection of the multi-organization step, executing a branch prediction algorithm to determine downstream granted organizations.

Abstract: An example operation includes one or more of detecting a fork in a supply-chain by a modeling node, resolving, by the modeling node, a branch prediction to determine a likely access control, generating, by the modeling node, a range of information based on a branch confidence level, and responsive to the resolution of the branch prediction, revoking access from a document or granting a greater access to the document based on the range.

Abstract: An example operation may include one or more of releasing, by a subscription service node, at least one blockchain transaction to a plurality of subscribing blockchain nodes requiring direct entitlement access, executing, by the subscription service node, a smart contract to calculate secondary entitlements triggered by the at least one blockchain transaction, determining, by the subscription service node, portions of second entitlement data sets allowed to be accessed by a subset of the plurality of the subscribing blockchain nodes, and sending verification data to the plurality of the subscribing blockchain nodes for verification of the second entitlement data sets.

Abstract: An example operation may include one or more of receiving, by a resolver node, asset data to be recorded on a blockchain, executing, by the resolver node, a smart contract to apply an asset data validation format to the asset data, rejecting, by the resolver node, non-conforming asset data from being stored on the blockchain based on the execution of the smart contract, and accepting the asset data validated by the smart contract.

Abstract: An example operation may include one or more of receiving, by a resolver node, from at least one user node assets to be recorded on a blockchain, executing, by the resolver node, a smart contract to reject non-conforming assets from being stored on the blockchain based on a set of rules defined by an asset validation format, determining, by the resolver node, events and transactions related to the rejected assets, and allowing the at least one user node to tag the rejected assets, the events and the transactions related to the rejected assets as contradictory.

Abstract: An example operation may include one or more of generating, by a blockchain node, an asset containment world-state by execution of a linear-time ingestion algorithm, detecting, by the blockchain node, an asset aggregation or disaggregation event, updating, by the blockchain node, the world-state based on the asset aggregation or disaggregation event, and computing supply-chain metrics based on the updated world-state.

Abstract: An example operation may include one or more of detecting, by a blockchain node, an asset aggregation or disaggregation event, performing, by the blockchain node, a single-shot update of an asset containment world-state at an ingestion of the asset aggregation or disaggregation event, determining, by the blockchain node, parent-child duration parameters for each instance of an asset parent-child association defined by the asset containment world-state, and executing a linear-time algorithm to calculate supply-chain metrics based on all combinations of the asset aggregation or disaggregation orderings based on the parent-child duration parameters.

Abstract: A computer system, method, and computer-readable product for automatically generating a hierarchy of concepts described in a video using semantic concept matching that assists persons in searching for specific types of content within videos. A video segment is obtained, with the segment having a distinct running time with discrete information related to one or more topics within its content. The discrete information of the video segment is then parsed out and related to a predetermined relevant topic, and then one or more attributes for weighting each discrete information relevant topic within the video segment are determined. A hierarchy of concepts of the video segments is then created, based upon, at least, the weighting of the information topic within the running time of the video segment based upon the presence of the one or more attributes. Other information related to the video segment can also be used to modify the hierarchy.

Abstract: An example operation may include one or more of receiving, by a processing node, a plurality of documents reflecting at least one action performed on supply-chain assets at a location of an asset supply-chain node, translating, by the processing node, the plurality of the documents into a plurality of entry and exit events of the supply-chain assets, and generating incomplete or non-chronological supply chain assets-related data based on the plurality of the entry and the exit events.

Abstract: An example operation may include one or more of receiving, by a first node, from a second node at least one document describing an action performed on assets of an asset supply-chain at a location of the second node, executing, by the first node, a smart contract to translate the at least one document into a standardized form of entry and exit events and to define containment relationship of the assets, and computing metrics on the asset supply-chain based on the standardized form and the containment relationship.

Abstract: An example operation may include one or more of receiving, by a first node, an order of goods from a user node, retrieving, by the first node, parameters of the order of the goods, connecting, by the first node, to a plurality of second nodes over a blockchain network, and executing, by the first node, a smart contract to: query the plurality of the second nodes for delivery options based on the parameters of the order of the goods, and allocate a delivery for the order of the goods to at least one second node of the plurality of the second nodes based on the delivery options.