Invention Grant
- Patent Title: Physical database design and tuning with deep reinforcement learning
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Application No.: US16728986Application Date: 2019-12-27
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Publication No.: US11593334B2Publication Date: 2023-02-28
- Inventor: Louis Martin Burger , Emiran Curtmola , Sanjay Nair , Frank Roderic Vandervort , Douglas P. Brown
- Applicant: Teradata US, Inc.
- Applicant Address: US CA San Diego
- Assignee: Teradata US, Inc.
- Current Assignee: Teradata US, Inc.
- Current Assignee Address: US CA San Diego
- Agency: Gates & Cooper LLP
- Main IPC: G06F16/20
- IPC: G06F16/20 ; G06F16/21 ; G06F16/22 ; G06N3/04 ; G06N3/08 ; G06F16/2453

Abstract:
An apparatus, method and computer program product for physical database design and tuning in relational database management systems. A relational database management system executes in a computer system, wherein the relational database management system manages a relational database comprised of one or more tables storing data. A Deep Reinforcement Learning based feedback loop process also executes in the computer system for recommending one or more tuning actions for the physical database design and tuning of the relational database management system, wherein the Deep Reinforcement Learning based feedback loop process uses a neural network framework to select the tuning actions based on one or more query workloads performed by the relational database management system.
Public/Granted literature
- US20210034588A1 PHYSICAL DATABASE DESIGN AND TUNING WITH DEEP REINFORCEMENT LEARNING Public/Granted day:2021-02-04
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