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ESTIMATING CARDINALITY SELECTIVITY UTILIZING ARTIFICIAL NEURAL NETWORKS

机译:利用人工神经网络估计基数选择性

摘要

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.
机译:可以接收包括谓词的数据库查询。每个谓词可以在数据库列上操作。可以确定数据库查询包括严格的运算符。可以定义上限神经网络以计算相邻的上限,并且可以定义下限神经网络以计算相邻的下限。可以使用从与要通过上限神经网络和下限神经网络执行的数据库查询相关联的数据库表的数据中选择的值来训练上限神经网络和下限神经网络。上限神经网络和下限神经网络可以通过使用表达式中发现的错误传入期望值来进行调整。可以响应于对数据库列的初始训练的完成来计算相邻的下限和相邻的上限。

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