Abstract: An example operation may include one or more of generating a proposal to perform a ledger operation at a first node, informing one or more second nodes of the proposal, receiving a decision on consensus among the first node and the one or more second nodes for the proposal, and performing the ledger operation at the first node when there is consensus, wherein the ledger operation changes a state database of a ledger of the first node and wherein the state database corresponds to a blockchain stored in the ledger.

Abstract: Providing targeted, evidence-based recommendations to improve digital content quality, in one aspect, may include partitioning digital content into a sequence of logical content modules. Quality scores for the logical content modules along a plurality of quality dimensions may be determined. User interactions with the digital content on a user interface are monitored. Raw events generated from user interaction in accessing the digital content are captured. Raw event patterns for the logical content modules are detected. The raw event patterns of user interaction are transformed into a set of dynamic states representing potential user experience and performance. The set of dynamic states are associated with a respective logical content module. The quality dimensions of the logical content modules and associated quality scores are correlated with the dynamic states.

Abstract: An example operation may include one or more of receiving a request to execute a cross-chain transaction, identifying disparate locations of two or more different blockchains that have stored therein data for the cross-chain transaction, retrieving data from data blocks of the two or more different blockchains, respectively, based on the identified disparate locations, executing the cross-chain transaction which takes the retrieved data from the two or more different blockchains as inputs to generate a cross-chain result, and storing the cross-chain result via a data block of a distributed ledger.

Abstract: An example operation may include one or more of storing, via a master chain, partition information that links together storage across a plurality of blockchains, receiving a request to execute a blockchain transaction from a client, determining whether the blockchain transaction is associated with data stored on one blockchain or data stored separately on different blockchains based on the partition information stored on the master chain, and, in response to a determination that the blockchain transaction is associated with data stored separately on different blockchains, identifying, via the master chain, a location of each blockchain from among the different blockchains and transmitting the locations to a system configured to perform the blockchain transaction.

Abstract: In an approach for integrating documents a processor extracts a first set of keywords from at least one structured document. A processor generates a first batch of keywords from the first set of keywords, wherein each keyword in the first batch of keywords includes a weight. A processor extracts a second set of keywords from at least one unstructured document. A processor compares the first batch of keywords to the second set of keywords. A processor determines that the at least one unstructured document matches, based on a predetermined threshold, the at least one structured document, based on the comparison of the first batch of keywords to the second set of keywords. A processor removes the at least one unstructured document from a list of unstructured documents which are to be processed.

Abstract: In an approach for integrating documents a processor extracts a first set of keywords from at least one structured document. A processor generates a first batch of keywords from the first set of keywords, wherein each keyword in the first batch of keywords includes a weight. A processor extracts a second set of keywords from at least one unstructured document. A processor compares the first batch of keywords to the second set of keywords. A processor determines that the at least one unstructured document matches, based on a predetermined threshold, the at least one structured document, based on the comparison of the first batch of keywords to the second set of keywords. A processor removes the at least one unstructured document from a list of unstructured documents which are to be processed.

Abstract: An example operation may include one or more of generating a proposal to perform a ledger operation at a first node, informing one or more second nodes of the proposal, receiving a decision on consensus among the first node and the one or more second nodes for the proposal, and performing the ledger operation at the first node when there is consensus, wherein the ledger operation changes a state database of a ledger of the first node and wherein the state database corresponds to a blockchain stored in the ledger.

Abstract: Providing targeted, evidence-based recommendations to improve digital content quality, in one aspect, may include partitioning digital content into a sequence of logical content modules. Quality scores for the logical content modules along a plurality of quality dimensions may be determined. User interactions with the digital content on a user interface are monitored. Raw events generated from user interaction in accessing the digital content are captured. Raw event patterns for the logical content modules are detected. The raw event patterns of user interaction are transformed into a set of dynamic states representing potential user experience and performance. The set of dynamic states are associated with a respective logical content module. The quality dimensions of the logical content modules and associated quality scores are correlated with the dynamic states.

Abstract: Providing targeted, evidence-based recommendations to improve digital content quality, in one aspect, may include partitioning digital content into a sequence of logical content modules. Quality scores for the logical content modules along a plurality of quality dimensions may be determined. User interactions with the digital content on a user interface are monitored. Raw events generated from user interaction in accessing the digital content are captured. Raw event patterns for the logical content modules are detected. The raw event patterns of user interaction are transformed into a set of dynamic states representing potential user experience and performance. The set of dynamic states are associated with a respective logical content module. The quality dimensions of the logical content modules and associated quality scores are correlated with the dynamic states.

Abstract: An example operation may include one or more of receiving a request to execute a cross-chain transaction, identifying disparate locations of two or more different blockchains that have stored therein data for the cross-chain transaction, retrieving data from data blocks of the two or more different blockchains, respectively, based on the identified disparate locations, executing the cross-chain transaction which takes the retrieved data from the two or more different blockchains as inputs to generate a cross-chain result, and storing the cross-chain result via a data block of a distributed ledger.

Abstract: An example operation may include one or more of storing, via a master chain, partition information that links together storage across a plurality of blockchains, receiving a request to execute a blockchain transaction from a client, determining whether the blockchain transaction is associated with data stored on one blockchain or data stored separately on different blockchains based on the partition information stored on the master chain, and, in response to a determination that the blockchain transaction is associated with data stored separately on different blockchains, identifying, via the master chain, a location of each blockchain from among the different blockchains and transmitting the locations to a system configured to perform the blockchain transaction.

Abstract: During application of data quality rules to a data set obtained from a data source, data is retrieved from the data source along with a common set of rules configured to format the retrieved data in a manner in accordance with one or more predefined data quality rules of the common set of rules. At least one predefined data quality rule is adjusted utilizing at least one editable widget to form a modified set of data quality rules adapted for use with a specified application. The modified set of data quality rules is applied to the retrieved data.

Abstract: During application of data quality rules to a data set obtained from a data source, data is retrieved from the data source along with a common set of rules configured to format the retrieved data in a manner in accordance with one or more predefined data quality rules of the common set of rules. At least one predefined data quality rule is adjusted utilizing at least one editable widget to form a modified set of data quality rules adapted for use with a specified application. The modified set of data quality rules is applied to the retrieved data.

Abstract: Computer program products and systems are provided for mining for sub-patterns within a text data set. The embodiments facilitate finding a set of N frequently occurring sub-patterns within the data set, extracting the N sub-patterns from the data set, and clustering the extracted sub-patterns into K groups, where each extracted sub-pattern is placed within the same group with other extracted sub-patterns based upon a distance value D that determines a degree of similarity between the sub-pattern and every other sub-pattern within the same group.

Abstract: The present invention relates to data cleansing, and in particular performing the semantic standardization process within a database before the transform portion of the extract-transform-load (ETL) process. Provided are a method, system and computer program product for standardizing data within a database engine, configuring the standardization function to determine at least one standardized value for at least one data value by applying the standardization table in a context of at least one data value, receiving a database query identifying the standardization function, at least one database value and the context of the data, and invoking the standardization function.

Abstract: Methods, systems, and computer program products for resolving textual numerical queries using natural language processing techniques are provided herein.

Abstract: Computer program products and systems are provided for mining for sub-patterns within a text data set. The embodiments facilitate finding a set of N frequently occurring sub-patterns within the data set, extracting the N sub-patterns from the data set, and clustering the extracted sub-patterns into K groups, where each extracted sub-pattern is placed within the same group with other extracted sub-patterns based upon a distance value D that determines a degree of similarity between the sub-pattern and every other sub-pattern within the same group.

Abstract: Techniques, systems, and articles of manufacture for collaborative analytics with edge devices. A method includes identifying multiple items of data pertaining to a user from one or more user actions implemented across multiple user devices, identifying one or more rules associated with one or more user preferences, exchanging the multiple items of data across the multiple user devices, and applying the one or more rules to the multiple items of data across the multiple user devices to generate an output via at least one of the multiple user devices.

Abstract: A method for supplementing structured information within a data system for entities based on unstructured data analyzes a document with unstructured data and extracts attribute values from the unstructured data for one or more entities of the data system. Entity records with structured information are retrieved from the data system based on the extracted attribute values. Entity references for corresponding entities of the data system are constructed based on a comparison of the retrieved entity records and the extracted attribute values. The entity references are linked to the corresponding entities within the data system, with the entity references including extracted attributes from the unstructured data for corresponding linked entities.

Abstract: Methods, systems, and computer program products for automated knowledge graph creation are provided herein.