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

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4 Author(s)
Shekar, S. ; Dept. of Comput. Sci., Minnesota Univ., Minneapolis, MN, USA ; Hamidzadeh, B. ; Kohli, A. ; Coyle, M.

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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Knowledge and Data Engineering, IEEE Transactions on  (Volume:5 ,  Issue: 6 )