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1. US20210406717 - ENABLING EFFICIENT MACHINE LEARNING MODEL INFERENCE USING ADAPTIVE SAMPLING FOR AUTONOMOUS DATABASE SERVICES

Office
United States of America
Application Number 16914816
Application Date 29.06.2020
Publication Number 20210406717
Publication Date 30.12.2021
Publication Kind A1
IPC
G06N 5/04
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
5Computer systems using knowledge-based models
04Inference methods or devices
G06N 20/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
G06F 16/22
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
20of structured data, e.g. relational data
22Indexing; Data structures therefor; Storage structures
CPC
G06F 16/2282
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
20of structured data, e.g. relational data
22Indexing; Data structures therefor; Storage structures
2282Tablespace storage structures; Management thereof
G06N 5/04
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
5Computer systems using knowledge-based models
04Inference methods or devices
G06N 20/00
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
Applicants Oracle International Corporation
Inventors Farhan Tauheed
Onur Kocberber
Tomas Karnagel
Nipun Agarwal
Title
(EN) ENABLING EFFICIENT MACHINE LEARNING MODEL INFERENCE USING ADAPTIVE SAMPLING FOR AUTONOMOUS DATABASE SERVICES
Abstract
(EN)

Herein are approaches for self-optimization of a database management system (DBMS) such as in real time. Adaptive just-in-time sampling techniques herein estimate database content statistics that a machine learning (ML) model may use to predict configuration settings that conserve computer resources such as execution time and storage space. In an embodiment, a computer repeatedly samples database content until a dynamic convergence criterion is satisfied. In each iteration of a series of sampling iterations, a subset of rows of a database table are sampled, and estimates of content statistics of the database table are adjusted based on the sampled subset of rows. Immediately or eventually after detecting dynamic convergence, a machine learning (ML) model predicts, based on the content statistic estimates, an optimal value for a configuration setting of the DBMS.


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