International Business Machines Corporation

Constantine Arnold

Deepavali M Bhagwat

Lukas Rupprecht

Wayne A Sawdon

Gregory Tad Kishi

Tanveer Fathima Syeda-Mahmood

Joseph W Dain

James C Davis

Dimitrios Skourtis

Nitin Ramchandani

A dataset formatted in a first format is generated, wherein the dataset formatted in the first format includes at least a plurality of data elements that comprise data. A selection is made of machine learning framework, wherein the machine learning framework operates on datasets formatted in a second format that is different from the first format. The dataset is converted from the first format to the second format and links are generated from the converted dataset formatted in the second format to the plurality of data elements of the dataset formatted in the first format to avoid creating additional copies of the plurality of data elements.

Generation of a dataset in the format of a machine learning framework

A computer-implemented method according to one embodiment includes monitoring for occurrence of trigger events corresponding to creation of metadata in a computing environment, and detecting occurrence of a trigger event. The method further includes loading a rule that is associated with the detected trigger event in response to detecting occurrence of the trigger event. The rule includes a condition and an action, and the action defines a subset of metadata associated with processing a job in the computing environment. The method further includes collecting the defined subset of metadata in accordance with the action in response to the condition of the rule being met, and storing the collected subset of metadata. A system according to another embodiment includes a processor, and logic integrated with the processor, executable by the processor, or integrated with and executable by the processor. The logic is configured to perform the foregoing method.

Rule-based collections of subset(s) of metadata in response to a trigger event occurring

In one embodiment, a computer-implemented method for namespace and content manipulation within a storage system includes: defining policy rule(s), each policy rule dictating: how namespaces within the storage system may be transformed; and/or how content stored within the storage system may be transformed; receiving a request for access to a particular data set stored within the storage system; parsing the request to determine one or more transformations requested therein and/or required thereby; determining whether the particular data set is compliant with the one or more transformations; in response to determining the particular data set is compliant with the one or more transformations, providing access to the particular data set; and in response to determining the particular data set is not compliant with the one or more transformations, performing the one or more transformations to generate a transformed data set, and providing access to the transformed data set.

Dynamic, transparent manipulation of content and/or namespaces within data storage systems

A mechanism is provided in a data processing system to implement a data mapping engine for transparent and declarative translation of search queries on documents to queries on relational data. The data mapping engine receives a query from a search framework for a target database and translates the query to a target schema based on a mapping definition data structure to form a translated query. The mapping definition data structure declaratively maps between a source schema of the search framework to a target schema of a target database. The data mapping engine sends the translated query to the target database and receives a response from the target database. The data mapping engine translates the response to the source schema based on the mapping definition data structure to form a translated response and sends the translated response to the search framework.

Mapping document data to relational data

A computer-implemented method according to one embodiment includes identifying an event within a system; applying a model to the event to create an aggregated event; and storing the aggregated event.

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San Jose, CA, United States of America

Constantine Arnold