Abstract: Aspects include processors configured to (or include program code that causes a processor to) provide for data classifier devices that extract from structured text business data inputs, via natural language understanding processing, training set data elements (for example, training keywords, training concepts, training entities, and/or training taxonomy classifications, etc.). The aspects identify associations within the structured training business data of each of a plurality of business class categories with respective ones of the extracted training set data elements; and build a logical relationship data classification training knowledge base ontology that connects ones of the business classes to respective associated ones of the extracted training data elements as questions, into a plurality of knowledge base ontology question-business class associations.

Abstract: Data classification extracts from structured text business data inputs, via natural language understanding processing, training set data elements (training keywords, training concepts, training entities, and/or training taxonomy classifications). Embodiments identify associations within the structured training business data of business class categories with respective ones of extracted training set data elements, and build a logical relationship data classification training knowledge base ontology that connects business classes to respective associated ones of extracted training data elements as questions into knowledge base ontology question-business class associations.

Abstract: A method, computer system, and a computer program product for data pipeline prioritization is provided. Embodiments may include receiving, by a cognitive rules engine, one or more data pipelines. Embodiments may then include analyzing, using a computational method of the cognitive rules engine, the one or more data pipelines. Embodiments may lastly include prioritizing the one or more data pipelines based on a result of the computational method of the cognitive rules engine.

Abstract: A method, computer system, and a computer program product for data pipeline prioritization is provided. Embodiments may include receiving, by a cognitive rules engine, one or more data pipelines. Embodiments may then include analyzing, using a computational method of the cognitive rules engine, the one or more data pipelines. Embodiments may lastly include prioritizing the one or more data pipelines based on a result of the computational method of the cognitive rules engine.

Abstract: Aspects include processors configured to (or include program code that causes a processor to) provide for data classifier devices that extract from structured text business data inputs, via natural language understanding processing, training set data elements (for example, training keywords, training concepts, training entities, and/or training taxonomy classifications, etc.). The aspects identify associations within the structured training business data of each of a plurality of business class categories with respective ones of the extracted training set data elements; and build a logical relationship data classification training knowledge base ontology that connects ones of the business classes to respective associated ones of the extracted training data elements as questions, into a plurality of knowledge base ontology question-business class associations.

Abstract: Data classification extracts from structured text business data inputs, via natural language understanding processing, training set data elements (training keywords, training concepts, training entities, and/or training taxonomy classifications). Embodiments identify associations within the structured training business data of business class categories with respective ones of extracted training set data elements, and build a logical relationship data classification training knowledge base ontology that connects business classes to respective associated ones of extracted training data elements as questions into knowledge base ontology question-business class associations.

Abstract: A source dataset is enriched by standardization of address data, date and time analysis, and demographic analysis. The enriched source dataset is used to form one or more distinct clusters that are unique combinations of values for one or more attributes of the enriched source dataset. One or more related datasets are found for each of the clusters, and the related datasets are merged into the enriched source dataset using a distributed join operation, wherein the distributed join allows each row of the source dataset to be joined with a different one of the related datasets, where the different one of the related datasets is closest to the cluster to which the row belongs.

Abstract: A source dataset is enriched by standardization of address data, date and time analysis, and demographic analysis. The enriched source dataset is used to form one or more distinct clusters that are unique combinations of values for one or more attributes of the enriched source dataset. One or more related datasets are found for each of the clusters, and the related datasets are merged into the enriched source dataset using a distributed join operation, wherein the distributed join allows each row of the source dataset to be joined with a different one of the related datasets, where the different one of the related datasets is closest to the cluster to which the row belongs.

Abstract: User-inferred data integration actions within tabular data. A user action with respect to a first portion of tabular data is detected. Examples of user action include a deletion, addition and/or modification in a row, column, cell or a combination thereof. The data integration tool may determine if the user action is a recognized action or a learned action, based on at least one type of the user action and at least one characteristic of the first portion of the tabular data. Suggests to the user an option to replay the recognized action or the learned action on a second portion of the tabular data, wherein the first portion and the second portion have at least one common characteristic. If the user action is neither a recognized action nor a learned action, the data integration tool suggests to the user an option to learn, or store, the user action in memory.

Abstract: A system, method and program product for analyzing performance of a system comprised of a database and its related operating environment. A system is provided that includes: a set of monitoring tools for monitoring event data from a database application and from an operating environment running the database application; a performance data warehouse for storing the event data; a modeling system for generating a performance mining model of the database system based on the event data stored in the performance data warehouse; and a system for comparing a stream of current event data against the performance mining model to identify performance issues in the database system.