Invention Grant
- Patent Title: Machine-learning-based, adaptive updating of quantitative data in database system
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Application No.: US17650399Application Date: 2022-02-09
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Publication No.: US11775516B2Publication Date: 2023-10-03
- Inventor: Ling Qin , Fu Fei Xu , Jie Zhang , Guo Dong Wan , Yi Lei Wang , Wen Jing Shi , Yuan Cao
- Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Applicant Address: US NY Armonk
- Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Current Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Current Assignee Address: US NY Armonk
- Agency: HESLIN ROTHENBERG FARLEY & MESITI P.C.
- Agent Teddi Maranzano, Esq.; Kevin P. Radigan, Esq.
- Main IPC: G06F16/245
- IPC: G06F16/245 ; G06N3/08 ; G06F16/25 ; G06F16/2453

Abstract:
Machine-learning-based, adaptive updating of quantitative data in a database system is provided, which includes training one or more machine learning models to facilitate adaptively updating quantitative data in the database system, and ascertaining an update urgency index for updating the quantitative data for one or more data structures of the database system. The update urgency index is representative of an urgency for updating the quantitative data for the data structure(s) and is based, at least in part, on real-time query metrics. The machine learning model(s) is used to adaptively update the quantitative data, where the adaptively updating is based, at least in part, on the ascertained update urgency index. Processing of a database query is optimized in the database system using the adaptively updated quantitative data.
Public/Granted literature
- US20230252024A1 MACHINE-LEARNING-BASED, ADAPTIVE UPDATING OF QUANTITATIVE DATA IN DATABASE SYSTEM Public/Granted day:2023-08-10
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