Date of Award
CC BY-NC-ND 4.0
Distributed query optimization is an important issue in distributed database management systems, since it can greatly affect the performance of the system. Many query optimization strategies have been proposed to minimize either the total cost or the response time. Most strategies are static in nature in the sense that their construction is based on database statistics which are obtained prior to query execution. In this thesis we investigate the use of dynamic strategies and better estimation techniques in query optimization. Bloom filters are used to obtain better estimates for query processing. Based on the above concept three algorithms are proposed, first using a pure dynamic strategy, the second using Bloom filters and the third using a combination of both. The performance of these algorithms with respect to total cost is compared against the AHY algorithm. The algorithms are executed against a large number of synthetically generated databases and queries. The experiments show a significant improvement over the AHY algorithm. The dynamic strategy shows an improvement over the static strategy and the combination heuristic shows a marginal improvement over the dynamic strategy. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1996 .K35. Source: Masters Abstracts International, Volume: 37-01, page: 0286. Adviser: Joan Morrissey. Thesis (M.Sc.)--University of Windsor (Canada), 1996.
Kamat, Sandeep., "Dynamic strategy and Bloom filters in distributed query optimization." (1996). Electronic Theses and Dissertations. 1800.