Abstract: An example operation includes one or more of establishing, by a first blockchain trust anchor node, a trusted connection to a trust anchor node of a second blockchain, detecting, by the first blockchain trust anchor node, changes of the first blockchain, and executing a smart contract to reflect the detected changes on the second blockchain.

Abstract: An example operation includes one or more of receiving, by a data processing node, inference data object from a multi-channel data server over a blockchain, sorting, by the data processing node, longitudinal records contained in the inference data object, linking, by the data processing node, transaction outcomes and inferences data from the inference data object to the sorted longitudinal records, and recording linked data onto a blockchain ledger. The data processing node serves as a validator of data from a robo-advisory using natural language (NL) processing to reduce bias and measure effectiveness of inference from the robo-advisory.

Abstract: A microservice join request is received by a first microservice from a second microservice within a microservices system. The microservice join request includes microservice trust relationship information of the second microservice that defines microservice credentials and service description parameters of the second microservice. Using the microservice trust relationship information, a determination is made as to whether a consensus exists among other microservices within the microservices system that the second microservice is authorized to inter-operate within the microservices system. In response to determining that the consensus exists, validated local run-time inter-operational microservice trust relationship information is created.

Abstract: Operating conditions of a blockchain configuration may be dynamic and change automatically under certain circumstances. One example method of operation may include one or more of identifying an existing consensus procedure used in an existing blockchain configuration, identifying current metrics associated with the existing blockchain configuration, comparing the current metrics to predefined rules, identifying one or more deviations based on the current metrics being compared to the predefined rules, and changing the existing consensus procedure to a next consensus procedure for a subsequent block in the existing blockchain configuration responsive to identifying the one or more deviations.

Abstract: An example operation may include one or more of identifying a blockchain transaction submitted from a requestor member of the blockchain, identifying one or more task requests associated with the blockchain transaction, determining one or more blockchain members having resources available for completing the one or more task requests, wherein the resources are identified via known computing parameters associated with each of the one or more blockchain members, and assigning the one or more task requests to the one or more blockchain members based on the resources available.

Abstract: An example operation may include one or more of connecting, by a scheduler node, to a blockchain network comprised of member nodes, receiving, by the scheduler node, a plurality of transactions that include deadlines from the member nodes, comparing, by the scheduler node, the deadlines of the plurality of the transactions against an average time to append to a ledger (ATAL) pre-calculated for the scheduler node, dropping, by the scheduler node, the transactions of the plurality of the transactions, if a sum of the ATAL and a current time is larger than the deadlines of the plurality of the transactions, calculating, by the scheduler node, a priority usage balance (PUB) for the member nodes based on the transactions of the plurality of transactions remaining after the transactions of the plurality of the transactions have been dropped, scheduling, by the scheduler node, a transaction with an earliest deadline from the plurality of the remaining transactions to be validated first for an execution, and arrangin

Abstract: Embodiments include a method for configuring a token transfer device for electronic transactions. The method can include requesting, by a token transfer device, one or more tokens having specified transaction exchange value; receiving, over a network, the one or more tokens; assigning, by the token transfer device, validation parameters indicating conditions under which the one or more tokens will be accepted in a transaction; determining, by the token transfer device, that one or more of the conditions have been met based on data provided by electronic components of the token transfer device; and initiating the transaction with a point of sale system.

Abstract: A blockchain of transactions may be referenced for various purposes and may be later accessed by interested parties for ledger verification. One example operation may comprise one or more of creating an initial identifier representing an asset and an owner entity of the asset in a blockchain, identifying an identity block associated with the initial identifier, and identifying an asset transfer of the asset from the owner entity to a blockchain entity.

Abstract: An example operation may include one or more of storing, within a blockchain, a request comprising an identifier of content and a value of a user who created the content, detecting consumption of a reusable instance of the content by a second user, designating fractional values of the content to the first and second users based on the detected consumption of the reusable instance, and storing, within the blockchain, a second request comprising an identifier of the reusable instance of the content, identifiers of the first and second users, and the designated fractional values of the content to the first and second users.

Abstract: Embodiments of the invention provide a method, system and computer program product for optimized continuous query processing in an in memory data grid (IMDG). In an embodiment of the invention, a method for optimized continuous query processing in an IMDG includes receiving a request to add an object to an IMDG. The method also includes evaluating a continuous query upon the object in order to produce a result. The method yet further includes adding an attribute to the object referencing the continuous query and assigning a value to the attribute of the result. The method even yet further includes inserting the object with the attribute into the IMDG. Finally, the method includes returning the value of the attribute in lieu of repeating an evaluation of the continuous query in response to receiving a request to repeat an evaluation of the continuous query in respect to the object.

Abstract: An example system may include a processor and memory, wherein the processor is configured to perform one or more of receive a first biometric sample of a user from a terminal, execute a smart contract to compare the first biometric sample against a pre-recorded second biometric sample stored on a blockchain, in response to the match, acquire shared data of the user from a communication service provider, execute a smart contract to generate a question based on the shared data, receive an answer to the question from the user and execute a smart contract to record the answer on the blockchain, and execute a smart contract to authenticate the user based on the answer to the question and the first biometric sample.

Abstract: An example system may include a processor and memory, wherein the processor is configured to perform one or more of acquire a first biometric sample of a user, compare the first biometric sample of the user against a pre-recorded second biometric sample, in response to the match, access shared data of the user stored on a blockchain, generate a question based on the shared data, receive an answer to the question from the user, and authenticate the user based on the answer to the question and the first biometric sample.

Abstract: An embodiment of the invention may include a method, computer program product and system for consensus optimization. The embodiment may include implementing a hierarchy of computing nodes. The hierarchy includes a plurality of levels. An nth level includes a plurality of subgroups of computing nodes. Each subgroup contains a leader computing node. An n+1 level includes all of the leader computing nodes of the nth level. The embodiment may include transmitting messages from a first computing node to a second computing node. The second computing node is within a same subgroup of the first computing node. The embodiment may include transmitting messages from the first computing node to a third computing node. The third computing node is a leader computing node from a different subgroup of the first computing node. The embodiment may include determining a consensus. The consensus is determined by computing nodes located at a highest level.

Abstract: An example operation may include one or more of executing a blockchain transaction which generates modifications to an asset, identifying a most recent version of the asset comprising most recent values of the asset before the blockchain transaction, generating a delta block for the executed blockchain transaction identifying differences between the modifications to the asset and the most recent version of the asset introduced by the blockchain transaction being executed while excluding unmodified values of the most recent version of the asset which are not modified by the executed blockchain transaction, and hashing the delta block to generate a hashed delta block and storing the hashed delta block on a distributed ledger.

Abstract: Embodiments of the invention provide a method, system and computer program product for data duplication detection in an in memory data grid (IMDG). A method for data duplication detection in an IMDG includes computing a hash value for each binary data value in a key value pair of a partition in an IMDG. The method also includes generating a map including an entry for each unique computed hash value and one or more keys corresponding to binary data values of respective key value pairs from which the hash value had been uniquely computed. Thereafter, only those hash values in the map with multiple keys associated therewith are identified and binary data corresponding to the multiple keys of the identified hash values are reported as potential duplicate data in the IMDG.

Abstract: An example operation may include one or more of receiving, by a pharmacy node, a request from a patient node for a prescription refill, the request contains a secret key of a patient, extracting, by the pharmacy node, the secret key from the request to verify a patient's identity, and executing, by the pharmacy node, a smart contract to: (a) decrypt a prescription data located on the ledger by an application of the secret key; (b) retrieve patient's allergy records from the ledger to check the allergy records against the prescription data; (c) determine a number of remaining refills from the prescription data; (d) check validity of the prescription data based on an expiration date; and commit a prescription refill transaction to the blockchain based on a successful execution of (b)-(d).

Abstract: An example operation may include one or more of connecting, by a pharmacy node, to a blockchain network configured to store patients' data on a blockchain ledger, receiving, by the pharmacy node, a request from a patient node for a prescription refill, the request contains a secret key of a patient, extracting, by the pharmacy node, the secret key from the request to verify a patient's identity, and executing, by the pharmacy node, a smart contract to: (a) decrypt a prescription data located on the ledger by an application of the secret key, (b) retrieve patient's allergy records from the ledger to check the allergy records against the prescription data, (c) determine a number of remaining refills from the prescription data, (d) check validity of the prescription data based on an expiration date, and commit a prescription refill transaction to the blockchain based on a successful execution of (b)-(d).

Abstract: An example operation may include one or more of acquiring, by a provision server, system policy data from a system policy engine, receiving, by the provision server, jurisdiction policy parameters from a jurisdiction policy engine, selecting, by the provision server, a smart contract from a smart contract repository based on the system policy data and the jurisdiction policy parameters, mapping, by the provision server, the smart contract to a node, and provisioning the node to a blockchain network.

Abstract: Creating sub-social networks based on contextual inferencing may include analyzing, using a processor, social network activity among a plurality of users that belong to a social network and determining, using the processor, an interest category according to the social network activity. A sub-social network for the interest category may be created using the processor. The sub-social network may include a subset of the plurality of users of the social network having an association with the interest category.

Abstract: According to one exemplary embodiment, a method for dynamic generation of payment token ratings is provided. The method includes receiving a token request, whereby the token request includes a plurality of metadata. The method also includes analyzing the received plurality of metadata based on at least one predefined rule set. The method further includes generating a token in response to receiving the token request. The method then includes determining an assurance rating for the generated token based on the analyzed received plurality of metadata, whereby the assurance rating indicates a risk of fraud associated with the generated token. The method finally includes assigning a token rating to the generated token based on the determined assurance rating.