US Patent Application 17734026. MATERIALIZED VIEW GENERATION AND PROVISION BASED ON QUERIES HAVING A SEMANTICALLY EQUIVALENT OR CONTAINMENT RELATIONSHIP simplified abstract

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MATERIALIZED VIEW GENERATION AND PROVISION BASED ON QUERIES HAVING A SEMANTICALLY EQUIVALENT OR CONTAINMENT RELATIONSHIP

Organization Name

MICROSOFT TECHNOLOGY LICENSING, LLC

Inventor(s)

Brandon Barry Haynes of Seattle WA (US)

Jyoti Leeka of Sunnyvale CA (US)

Anna Pavlenko of Edmonds WA (US)

Rana Alotaibi of Laguna Beach CA (US)

Alekh Jindal of Sammamish WA (US)

MATERIALIZED VIEW GENERATION AND PROVISION BASED ON QUERIES HAVING A SEMANTICALLY EQUIVALENT OR CONTAINMENT RELATIONSHIP - A simplified explanation of the abstract

This abstract first appeared for US patent application 17734026 titled 'MATERIALIZED VIEW GENERATION AND PROVISION BASED ON QUERIES HAVING A SEMANTICALLY EQUIVALENT OR CONTAINMENT RELATIONSHIP

Simplified Explanation

- This patent application is about generating and returning materialized views for queries that have a specific relationship with each other. - Machine learning techniques are used to identify query subexpressions that are either semantically equivalent or contain each other. - When such relationships are identified, a materialized view is created for the identified subexpressions. - When a query is received, machine learning techniques are used to determine if a subexpression of the query has a relationship with another subexpression that has a materialized view. - If a relationship is found, the materialized view generated for the other subexpression is returned.


Original Abstract Submitted

Embodiments described herein are directed to generating and returning materialized views for queries (or subexpressions thereof) having a particular relationship with each other. For instance, machine learning-based techniques may be utilized to identify query subexpressions that have at least one of a semantically equivalent relationship or a containment relationship with each other. Responsive to identifying such relationship(s), a materialized view may be generated for the identified subexpressions. When a query is subsequently received, machine learning-based techniques may be utilized to determine whether a subexpression of the query possesses at least one of a semantically equivalent relationship or a containment relationship with another subexpression for which a materialized view has been generated. Responsive to determining that such a subexpression of the query possesses one or more of such relationships, the materialized view generated for the other subexpression is returned.

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