Title :
Learning transformation rules for semantic query optimization: a data-driven approach
Author :
Shekar, S. ; Hamidzadeh, Babak ; Kohli, Ashim ; Coyle, Mark
Author_Institution :
Dept. of Comput. Sci., Minnesota Univ., Minneapolis, MN, USA
fDate :
12/1/1993 12:00:00 AM
Abstract :
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
Keywords :
computational complexity; deductive databases; learning (artificial intelligence); query processing; SQO; closure algorithm; completeness; complexity; correctness; data distribution; data-driven approach; data-driven discovery; query-transformation rules; semantic query optimization; transformation rules; Computer science; Cost function; Data analysis; Data mining; Database systems; Indexes; Query processing; Transportation;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
