Learning Database Abstractions for Query Reformation
Abstract
The query reformulation approach takes advantage of the semantic knowledge about the contents of databases for optimization. The basic idea is to use the knowledge to reformulate a query into a less expensive yet equivalent query. Previous work on semantic query optimization has shown the cost reduction that can be achieved by reformulation, we further point out that when applied to distributed multibase queries, the reformulation approach can reduce the cost of moving intermediate data from one site to another. However, a robust and efficient method to discover the required knowledge has not yet been developed. This paper presents an example-guided, data-driven learning approach to acquire the knowledge needed in reformulation We use example queries to guide the learning to capture the database usage pattern. In contrast to the heuristic- driven approach is more likely to learn the required knowledge for the various reformulation needs of the example queries. Since this learning approach minimizes the dependency on the database structure and implementation, it is applicable to heterogeneous multidatabase systems. Query reformulation, Semantic query, Optimization, Inductive learning, Database abstractions, SIMS.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 30, 1993
- Accession Number
- ADA269531
Entities
People
- Chun-Nan Hsu
- Craig Knoblock
Organizations
- University of Southern California