International Business Machines Corporation

Meichi M Lin

Nigel G Slinger

John Benjamin Tobler

Daniel Kozin

Ke Wei Wei

Arthur Marais

Shuang Ys Yu

Wen J Zhu

Wei Liu

Hong Min

Qi Zhang

Peter Bendel

Heng Liu

Wen Jie Zhu

Yi Dai

Guo Ran Sun

An approach is provided in which a method, system, and program product analyze, while training a machine learning model, a set of first data transformation operators in a first data preparation pipeline that generates a plurality of constructed features from a set of training data. The method, system, and program product create a plurality of second data preparation pipelines from the first data preparation pipeline, wherein the set of first data transformation operators are converted to a set of second data transformation operators and each assigned to one of the plurality of second data preparation pipelines. The method, system, and program product deploy the plurality of second data preparation pipelines to a runtime system.

Auto feature preparation for high performance online inferencing

A method includes generating a plurality of vectors representing words in a plurality of documents about an information technology (IT) system and clustering the plurality of vectors to produce a plurality of clusters. The method also includes identifying a cluster of the plurality of clusters that contains a plurality of clustered vectors, generating a feature based on a plurality of words represented by the plurality of clustered vectors, and training a machine learning model to identify an anomaly in the IT system based on the feature.

Automatic anomaly detection

In an embodiment, a method of resource pre-configuration includes obtaining, at a target system, configuration information about a resource that is predicted to be used by a transaction which will be executed on the target system. The resource is configured, at the target system, based on the configuration information. The configuring is before the transaction requests use of the resource.

Resource pre-configuration

According to one embodiment of the present invention, a system inserts data into a database object. The system associates the database object with a parameter specifying materialization of data for the database object. The system inserts data into the database object and materializes the data in accordance with the parameter to provide access to the data from the database object, wherein the parameter specifies a portion of the data to be materialized upon insertion. Embodiments of the present invention further include a method and computer program product for inserting data into a database object in substantially the same manners described above.

Database insert with deferred materialization

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San Jose, CA, United States of America

Meichi M Lin