Learning transformation rules for semantic query optimization: a data-driven approach

An approach to learning query-transformation rules based on analyzing the existing data in the database is proposed. A framework and a closure algorithm for learning rules from a given data distribution are described. The correctness, completeness, and complexity of the proposed algorithm are charac...

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Bibliographic Details
Published inIEEE transactions on knowledge and data engineering Vol. 5; no. 6; pp. 950 - 964
Main Authors Shekar, S., Hamidzadeh, B., Kohli, A., Coyle, M.
Format Journal Article
LanguageEnglish
Published IEEE 01.12.1993
Subjects
Computer science
Cost function
Data analysis
Data mining
Database systems
Indexes
Query processing
Transportation
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ISSN1041-4347
DOI10.1109/69.250077

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Summary:An approach to learning query-transformation rules based on analyzing the existing data in the database is proposed. A framework and a closure algorithm for learning rules from a given data distribution are described. The correctness, completeness, and complexity of the proposed algorithm are characterized and a detailed example is provided to illustrate the framework.< >
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ISSN:1041-4347
DOI:10.1109/69.250077