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Issue No.06 - December (1993 vol.5)
pp: 950-964
<p>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.</p>
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
S. Shekhar, B. Hamidzadeh, A. Kohli, M. Coyle, "Learning Transformation Rules for Semantic Query Optimization: A Data-Driven Approach", IEEE Transactions on Knowledge & Data Engineering, vol.5, no. 6, pp. 950-964, December 1993, doi:10.1109/69.250077
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