Teradata Us, Inc.

Preeti Javaji

Douglas Paul Brown

Gregory Howard Milby

Steven Bart Cohen

Yu Long

Judy Wu

A machine-learning driven Database Management System (DBMS) is provided. One or more machine-learning algorithms are trained on the database constructs and execution plans produced by a database optimizer for queries. The trained machine-learning algorithms provide predictors when supplied the constructs and plans for a given query. The predictors are processed by the DBMS to make resource, scheduling, and Service Level Agreement (SLA) compliance decisions with respect to the given query.

Machine-learning driven database management

Various embodiments herein each include at least one of systems, methods, and software for generating, storing, and using non-unique secondary indexes of semi-structured data in database tables. One method embodiment includes creating and storing a non-unique secondary index (NUSI) for a database table based on column data stored in rows of the database table. The NUSI in some such embodiments identifying an offset to specific data included in the column data of at least some rows of the database table where the specific data is located. Other embodiments include building a NUSI index that includes values embedded therein.

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San Diego, CA, United States of America

Preeti Javaji