LEARNING TRANSFORMATION RULES FOR SEMANTIC QUERY OPTIMIZATION - A DATA-DRIVEN APPROACH

doi:10.1109/69.250077

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LEARNING TRANSFORMATION RULES FOR SEMANTIC QUERY OPTIMIZATION - A DATA-DRIVEN APPROACH

1993-01-01

PRE

SHEKHAR, S *

Babak Hamidzadeh

Mark Coyle

DOI：10.1109/69.250077

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Abstract

En 中文

Learning query-transformation rules are vital for the success of semantic query optimization in domains where the user cannot provide a comprehensive set of integrity constraints. Finding these rules is a discovery task because of the lack of targets. Previous approaches to learning query-transformation rules have been based on analyzing past queries. We propose a new approach to learning query-transformation rules based on analyzing the existing data in the database. This paper describes a framework and a closure algorithm for learning rules from a given data distribution. We characterize the correctness, completeness, and complexity of the proposed algorithm and provide a detailed example to illustrate the framework.

Keywords:

DATA

DISCOVERY IN DATABASES

LEARNING

RULE DISCOVERY

SEMANTIC QUERY OPTIMIZATION

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