Consideration of interdependencies in the relational database system, and, A proposal and evaluation of an expert system for the relational database structure

This thesis addresses the issue of interdependencies in Distributed and non-Distributed Relational Database Management Systems and proposes a design and development of an Expert System to manage and enhance the current available Database Structures; In the first part, we study, compare and evaluate the interdependencies found in the operating environment relevant to the Distributed Relational structure. Hardware and software configurations are grouped and compared in an attempt to understand the interdependencies of the system so that an optimal configuration may be obtained; In the second part, we designed and developed an Expert System configuration with ease of use and functionality as foremost concerns. The system reuses the transient tables used to service queries to achieve a performance improvement without explicit user knowledge. Basic fragmentation principles are also used to aid in performance by implicitly restructuring the tables within a database to balance access time. (Abstract shortened with permission of author.)

Robust and Skew-resistant Parallel Joins in Shared-Nothing Systems

The performance of joins in parallel database management systems is critical for data intensive operations such as querying. Since data skew is common in many applications, poorly engineered join operations result in load imbalance and performance bottlenecks. State-of-the-art methods designed to handle this problem offer significant improvements over naive implementations. However, performance could be further improved by removing the dependency on global skew knowledge and broadcasting. In this paper, we propose PRPQ (partial redistribution & partial query), an efficient and robust join algorithm for processing large-scale joins over distributed systems. We present the detailed implementation and a quantitative evaluation of our method. The experimental results demonstrate that the proposed PRPQ algorithm is indeed robust and scalable under a wide range of skew conditions. Specifically, compared to the state-of-art PRPD method, we achieve 16% - 167% performance improvement and 24% - 54% less network communication under different join workloads

Dynamic strategy and Bloom filters in distributed query optimization.

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