Machines During the past decade, parallel database systems havegained increased popularity due to their high performance,scalability, and availability characteristics. With thepredicted future database sizes and complexity of queries,it is essential that these systems effectively utilizehundreds or thousands of processors. Several studies haverepeatedly demonstrated that both the performance and scalability of a parallel database system is contingent on the physical layout of the data across the processors of the system.Recently, the design of a new declustering strategy, termed Multi-Attribute GrId deClustering (MAGIC), was introduced. MAGIC is superior to the current declustering strategies because it can partition a relation using two or more of its attributes. In addition, it minimizes the overhead of using parallelism to allow the system to scale to a large number of processors. This project focuses on: (1) analysis and extension of MAGIC for executing complex queries, and (2) incorporation of algorithms for dynamic on-line reorganization of a relation in the presence of update queries and changing workload characteristics.The approach involves development of analytical and simulation models first, followed by a prototype implementation. This research enhances the scalability of a parallel database machine and improve its performance for executing complex queries.
