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LEARNING TRANSFORMATION RULES FOR SEMANTIC QUERY OPTIMIZATION - A DATA-DRIVEN APPROACH
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DOI：10.1109/69.250077.png)
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摘要
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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.
Keyword:
DATA
DISCOVERY IN DATABASES
LEARNING
RULE DISCOVERY
SEMANTIC QUERY OPTIMIZATION
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期刊
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10.4
论文数:
6.8K
被引数:
3.2W
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