Abstract: User interactions with a supply chain system are monitored based on a tracked ontology enrichment process, an explainable reasoning graph is constructed based on the monitored user interactions and domain specific reasoning information; and an explainable insight of the monitored user interactions is learned, as is a user interaction embedding for an embedding space, based on the constructed explainable reasoning graph and the explainable insight. External data is incorporated into the embedding space, a joint embedding is learned based on the user interaction embedding, and missing entities and relationships are identified for incorporation into an ontology based on the user interactions and joint embedding. The ontology is revised to incorporate the missing entities and relationships into the ontology to create a revised ontology, and a supply chain is controlled based on the revised ontology.

Abstract: In an approach to improve order management by performing sustainable order fulfillment optimization through computer analysis, embodiments receive an order from a user through the order management system for performing sustainable order fulfillment. Further, embodiments estimate carbon emissions and economic costs of fulfilling the order from a plurality of nodes, and output an optimal sustainable order fulfillment. Additionally, responsive to receiving confirmation to implement the output optimal sustainable order fulfillment, embodiments place the optimal sustainable order fulfillment for the received order.

Abstract: Methods, systems, and computer program products for dynamically enhancing supply chain strategies based on carbon emission targets are provided herein.

Abstract: In a given computing node comprising at least one processor operatively coupled to a memory and implemented in a given vehicle, the method comprises obtaining at the given computing node data from at least one of: (i) one or more sensors on the given vehicle; (ii) one or more computing nodes associated with one or more other vehicles via at least one vehicle-to-vehicle communication protocol; and (iii) one or more data sources along the path of the given vehicle. The given computing node utilizes at least a portion of the obtained data to compute a risk assessment for the given vehicle with respect to an operational safety level of a proposed vehicle action. The given computing node initiates or prevents the proposed vehicle action for the given vehicle based on the computed risk assessment.

Abstract: In a given computing node comprising at least one processor operatively coupled to a memory and implemented in a given vehicle, the method comprises obtaining at the given computing node data from at least one of: (i) one or more sensors on the given vehicle; (ii) one or more computing nodes associated with one or more other vehicles via at least one vehicle-to-vehicle communication protocol; and (iii) one or more data sources along the path of the given vehicle. The given computing node utilizes at least a portion of the obtained data to compute a risk assessment for the given vehicle with respect to an operational safety level of a proposed vehicle action. The given computing node initiates or prevents the proposed vehicle action for the given vehicle based on the computed risk assessment.

Abstract: A system for acquiring road data includes a resource analyzer for analyzing resources used by a device on a vehicle, a context analyzer for setting a context for data valuation and decision making based the analyzed resources, a data evaluator for evaluating data based on the set context, a decision engine for determining data to acquire based on the set context, and a data acquisition device for acquiring the determined data.