Enabling efficient machine learning model inference using adaptive sampling for autonomous database services
US12014286B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 29, 2020 |
| Grant date | Jun 18, 2024 |
| Priority date | — |
| Expiry date | Nov 30, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.