Abstract: An example operation may include one or more of receiving a request to execute a blockchain transaction from a user device that is associated with a user account, determining whether to perform the blockchain transaction based on a dynamic participation policy of the user account, the dynamic participation policy being dynamically determined based on previous blockchain transactions of one or more of the user account and of other user accounts, and, in response to determining to perform the blockchain transaction, executing the blockchain transaction on behalf of the user account via a smart contract and storing information about the executed blockchain transaction.

Abstract: An example operation may include one or more of receiving, by a document validation node, documents from a plurality of document owner nodes over a blockchain network, generating, by the document validation node, commitments for the documents on the blockchain network, deriving, by the document validation node, proofs to verify predicates of the documents, and generating, by the document validation node, a document relationship graph (DRG) based on the commitments and the predicates.

Abstract: An example operation may include one or more of receiving, by a document validation node, documents from a plurality of document owner nodes over a blockchain network, generating, by the document validation node, commitments for the documents on the blockchain network, deriving, by the document validation node, proofs to verify predicates of the documents, and generating, by the document validation node, a document relationship graph (DRG) based on the commitments and the predicates.

Abstract: An example operation may include one or more of receiving a request to execute a blockchain transaction from a user device that is associated with a user account, determining whether to perform the blockchain transaction based on a dynamic participation policy of the user account, the dynamic participation policy being dynamically determined based on previous blockchain transactions of one or more of the user account and of other user accounts, and, in response to determining to perform the blockchain transaction, executing the blockchain transaction on behalf of the user account via a smart contract and storing information about the executed blockchain transaction.

Abstract: An integrated supply chain management with anomaly detection. An order schedule has one or more orders, each of which has a production requirement and a due date. An asset schedule has asset commitments associating assets with orders. Each asset has equipment specifications, including an asset class and one or more operational thresholds. The computer system identifies an asset of a class corresponding to a production requirement of an order and modifies the asset schedule to commit the asset to the order prior to the due date of the order. The computer system receives sensor input for the asset and determines whether an anomaly exists. If so, the computer system commits a second asset to the order.

Abstract: A computer implemented method, computer program product and system for predicting an outcome of the execution of a schedule. The schedule, endogenous data relating to one or more activities or the schedule, and exogenous data relating to one or more activities or the schedule is processed in accordance with a machine learning algorithm-generated prediction model to predict at least one of: a performance measure, and a final condition that may result from executing the one or more activities according to the schedule.

Abstract: An integrated supply chain management with anomaly detection. An order schedule has one or more orders, each of which has a production requirement and a due date. An asset schedule has asset commitments associating assets with orders. Each asset has equipment specifications, including an asset class and one or more operational thresholds. The computer system identifies an asset of a class corresponding to a production requirement of an order and modifies the asset schedule to commit the asset to the order prior to the due date of the order. The computer system receives sensor input for the asset and determines whether an anomaly exists. If so, the computer system commits a second asset to the order.

Abstract: An integrated supply chain management with anomaly detection. An order schedule has one or more orders, each of which has a production requirement and a due date. An asset schedule has asset commitments associating assets with orders. Each asset has equipment specifications, including an asset class and one or more operational thresholds. The computer system identifies an asset of a class corresponding to a production requirement of an order and modifies the asset schedule to commit the asset to the order prior to the due date of the order. The computer system receives sensor input for the asset and determines whether an anomaly exists. If so, the computer system commits a second asset to the order.