Invention Grant
- Patent Title: Estimating cardinality selectivity utilizing artificial neural networks
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Application No.: US15148444Application Date: 2016-05-06
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Publication No.: US10706354B2Publication Date: 2020-07-07
- Inventor: Vincent Corvinelli , Huaxin Liu , Mingbin Xu , Ziting Yu , Calisto P. Zuzarte
- 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
- Agent Jay Wahlquist
- Main IPC: G06N3/02
- IPC: G06N3/02 ; G06N3/08 ; G06N3/04 ; G06F16/2453 ; G06N5/00

Abstract:
A database query comprising predicates may be received. Each predicate may operate on database columns. The database query may be determined to comprise strict operators. An upper bound neural network may be defined for calculating an adjacent upper bound and a lower bound neural network may be defined for calculating an adjacent lower bound. The upper bound neural network and the lower bound neural network may be trained using a selected value from data of a database table associated with the database query to be executed through the upper bound neural network and the lower bound neural network. The upper bound neural network and the lower bound neural network may be adjusted by passing in an expected value using an error found in expressions. The adjacent lower bound and the adjacent upper bound may be calculated in response to completion of initial training for the database columns.
Public/Granted literature
- US20170323200A1 ESTIMATING CARDINALITY SELECTIVITY UTILIZING ARTIFICIAL NEURAL NETWORKS Public/Granted day:2017-11-09
Information query