Microsoft Technology Licensing, LLC

Jyoti Leeka

Yuanyuan Tian

Alekh Jindal

Brandon Barry Haynes

Marc Todd Friedman

Carlo Aldo Curino

Hiren Shantilal Patel

Shi Qiao

Tarique Ashraf Siddiqui

Jesus Camacho Rodriguez

Anna Pavlenko

Rana Alotaibi

Hanxian Huang

Sunny Gakhar

Rana Bijad M Alotaibi

Examples detect equivalent subexpressions within a computational workload. Examples include converting a query plan tree associated with a first subexpression into a matrix. The first subexpression is a portion of a database query from the computational workload. Each node in the query plan tree is represented as a row of the matrix. The matrix is converted into a first vector. The first subexpression is determined to be equivalent to a second subexpression by comparing the first vector to a second vector associated with the second subexpression. The comparison includes computing a distance between the first and second vectors that is lower than a distance threshold. The computational workload is modified, based on the determining, to perform the first subexpression and exclude performance of the second subexpression as duplicative.

Machine learning accelerated semantic equivalence detection

Systems and methods for characterizing and forecasting evolving query workloads. The method includes receiving a query, the received query including a parameter value and an arrival time; identifying the query as a recurrent query; extracting a query template from the received query by parsing the received query; based at least on the identifying, generating a feature vector for the received query, the feature vector generated based on the extracted template and the parameter value; and forecasting a future query based on the generated feature vector by applying a neural network.

Characterizing and forecasting evolving query workloads

In a set of data analytics queries, at least a one of the queries comprising more than one operator, and each query being at least one of i) a producer of data for an other query in the set, and ii) a consumer of data from an other query in the set. In such examples, one or more computing devices identify each producer/consumer relationship between the queries. The one or more computing devices identify one or more optimizations among the queries based on the identified relationships. The one or more computing devices then apply at least one identified optimization to at least one of the queries.

Query set optimization in a data analytics pipeline

Growing community of inventors

Sunnyvale, CA, United States of America

Jyoti Leeka