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: An approach is provided for autonomous and optimal cloning, reinstating, and archiving of a containerized application. Application metadata is obtained from a catalog. Cloning rules specifying cloning an application are obtained. Application components are selected for cloning and the cloning is determined to be compliant with the cloning rules. The application components are cloned and data for the clone is saved to a clone repository. The catalog is updated with specifications of the cloned application components. Reinstating rules specifying reinstating a clone of the application are obtained. Reinstating the clone is determined to be compliant with the reinstating rules. The catalog is updated with specifications of the reinstated clone. In one embodiment, the catalog is updated with inferred associations among applications and identified rules associated with the application, where the inferred associations and identified rules are generated by a trained machine learning-based classifier.

Abstract: An approach is provided for autonomous and optimal cloning, reinstating, and archiving of a containerized application. Application metadata is obtained from a catalog. Cloning rules specifying cloning an application are obtained. Application components are selected for cloning and the cloning is determined to be compliant with the cloning rules. The application components are cloned and data for the clone is saved to a clone repository. The catalog is updated with specifications of the cloned application components. Reinstating rules specifying reinstating a clone of the application are obtained. Reinstating the clone is determined to be compliant with the reinstating rules. The catalog is updated with specifications of the reinstated clone. In one embodiment, the catalog is updated with inferred associations among applications and identified rules associated with the application, where the inferred associations and identified rules are generated by a trained machine learning-based classifier.

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: 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.