公开(公告)号：US11138266B2

公开(公告)日：2021-10-05

申请号：US16282116

申请日：2019-02-21

Applicant: Microsoft Technology Licensing, LLC

Inventor： Bailu Ding ,  Sudipto Das ,  Surajit Chaudhuri ,  Vivek R Narasayya ,  Ryan Marcus ,  Lin Ma ,  Adith Swaminathan

IPC: G06F7/02 ,  G06F16/00 ,  G06F16/903 ,  G06F16/906 ,  G06F16/901 ,  G06N20/00

Abstract: Systems and techniques for leveraging query executions to improve index recommendations are described herein. In an example, a machine learning model is adapted to receive a first query plan and a second query plan for performing a query with a database, where the first query plan is different from the second query plan. The machine learning model may be further adapted to determine execution cost efficiency between the first query plan and the second query plan. The machine learning model is trained using relative execution cost comparisons between a set of pairs of query plans for the database. The machine learning model is further adapted to output a ranking of the first query plan and second query plan, where the first query plan and second query plan are ranked based on execution cost efficiency.

公开(公告)号：US20200272667A1

公开(公告)日：2020-08-27

申请号：US16282116

申请日：2019-02-21

Applicant: Microsoft Technology Licensing, LLC

Inventor： Bailu Ding ,  Sudipto Das ,  Surajit Chaudhuri ,  Vivek R Narasayya ,  Ryan Marcus ,  Lin Ma ,  Adith Swaminathan

IPC: G06F16/903 ,  G06F16/901 ,  G06F16/906 ,  G06N20/00

Abstract: Systems and techniques for leveraging query executions to improve index recommendations are described herein. In an example, a machine learning model is adapted to receive a first query plan and a second query plan for performing a query with a database, where the first query plan is different from the second query plan. The machine learning model may be further adapted to determine execution cost efficiency between the first query plan and the second query plan. The machine learning model is trained using relative execution cost comparisons between a set of pairs of query plans for the database. The machine learning model is further adapted to output a ranking of the first query plan and second query plan, where the first query plan and second query plan are ranked based on execution cost efficiency.

公开(公告)号：US20170308535A1

公开(公告)日：2017-10-26

申请号：US15136688

申请日：2016-04-22

Applicant: Microsoft Technology Licensing, LLC

Inventor： Alekh Agarwal ,  Miroslav Dudik ,  Akshay Krishnamurthy ,  John Langford ,  Adith Swaminathan

IPC: G06F17/30 ,  G06N7/00

CPC classification number: G06F16/24578 ,  G06F16/248 ,  G06F16/3326 ,  G06F16/337 ,  G06F16/9535 ,  G06N7/005

Abstract: A computing device can determine a decomposition of data of actions of a first session based at least in part on a first computational model associating the actions of the first session with corresponding state values of the first session. The computing device can determine a second computational model based at least in part on the decomposition and an operation template. The computing device can receive a query via the communications interface, the query associated with the second session. The computing device can determine a state value of the second session based at least in part on the query. The computing device can operate the second computational model to determine at least one response associated with the query based at least in part on the state value of the second session. The computing device can provide an indication of the at least one response via the communications interface.