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Learning Transformation Rules for Semantic Query Optimization: A Data-Driven Approach
December 1993 (vol. 5 no. 6)
pp. 950-964

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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Index Terms:
transformation rules; semantic query optimization; data-driven approach; query-transformation rules; closure algorithm; data distribution; correctness; completeness; complexity; SQO; data-driven discovery; computational complexity; deductive databases; learning (artificial intelligence); query processing
Citation:
S. Shekhar, B. Hamidzadeh, A. Kohli, M. Coyle, "Learning Transformation Rules for Semantic Query Optimization: A Data-Driven Approach," IEEE Transactions on Knowledge and Data Engineering, vol. 5, no. 6, pp. 950-964, Dec. 1993, doi:10.1109/69.250077
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