International Business Machines Corporation

Jo Arao Ramos

Qiqing Christine Ouyang

Namit Kabra

Eric Allen Jacobson

Ritesh Kumar Gupta

Scott Louis Brokaw

Deepak Rangarao

Marcio T Moura

John Baxter Rollins

Likhitha Maddirala

David Giddens Wilhite

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.

Data classification for data lake catalog

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.

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.

Prioritization and prediction of jobs using cognitive rules engine

A self-learning computer-based system has access to multiple runtime modules that are each capable of performing a particular algorithm. Each runtime module implements the algorithm with different code or runs in a different runtime environment. The system responds to a request to run the algorithm by selecting the runtime module or runtime environment that the system predicts will provide the most desirable results based on parameters like accuracy, performance, cost, resource-efficiency, or policy compliance. The system learns how to make such predictions through training sessions conducted by a machine-learning component. This training teaches the system that previous module selections produced certain types of results in the presence of certain conditions. After determining whether similar conditions currently exist, the system uses rules inferred from the training sessions to select the runtime module most likely to produce desired results.

Self-learning selection of information-analysis runtimes

Methods and apparatus, including computer program products, implementing and using techniques for populating a data cache on a server. Data requests received by the server are collected in a repository. A data mining algorithm is applied to the collected data requests to predict a set of data that is likely to be requested during an upcoming time period. It is determined whether the complete set of predicted data exists in the data cache. If the complete set of predicted data does not exist in the data cache, the missing data is retrieved from a database and added to the data cache.

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Jo Arao Ramos