Accession Number : ADA308791

Title :   Using Inductive Learning to Generate Rules for Semantic Query Optimization.

Descriptive Note : Research rept.,

Corporate Author : UNIVERSITY OF SOUTHERN CALIFORNIA MARINA DEL REY INFORMATION SCIENCES INST

Personal Author(s) : Hsu, Chun-Nan ; Knoblock, Craig A.

PDF Url : ADA308791

Report Date : JUN 1995

Pagination or Media Count : 22

Abstract : Semantic query optimization can dramatically speed up database query answering by knowledge intensive reformulation. But the problem of how to learn the required semantic rules has not been previously solved. This report presents a learning approach to solving this problem. In our approach, the learning is triggered by user queries. Then the system uses an inductive learning algorithm to generate semantic rules. This inductive learning algorithm can automatically select useful join paths and attributes to construct rules from a database with many relations. The learned semantic rules are effective for optimization because they will match query patterns and reflect data regularities. Experimental results show that this approach learns sufficient rules for optimization that produces a substantial cost reduction.

Descriptors :   *OPTIMIZATION, *SEMANTICS, *INTERROGATION, DATA BASES, ALGORITHMS, PROBLEM SOLVING, REDUCTION, COSTS, USER NEEDS, LEARNING.

Subject Categories : Linguistics

Distribution Statement : APPROVED FOR PUBLIC RELEASE