The goal of this proposal is to develop new effective methods to improve the performance of sets of frequent and important queries on large relational databases, which could improve the efficiency of user interactions with data-management systems. Solving the problem will have the most effect in query optimization, data warehousing, and information integration. The project focuses on the methodology of evaluating queries using views; views are relations that are defined by auxiliary queries and can be used to rewrite and answer user queries. One way to improve query performance is precompute and store (i.e., "materialize") views. To truly optimize query performance, it is critical to materialize the "right" views. The project will demonstrate that, by designing and materializing views, it is possible to ensure optimal or near-optimal performance of frequent and important queries, for common and important query types. The focus of this effort is on developing efficient and scalable heuristic algorithms that design (near-) optimal sets of views for the given queries. The project has two parts: (1) theoretical analysis and design of algorithms and heuristics for view design, and (2) implementation and experiments on large databases, to evaluate the performance improvements caused by using the views. The techniques resulting from this project could have application in commercial and experimental database systems, where they will provide new ways to lower query-processing costs. The research results will be accessible via a project web site http://research.csc.ncsu.edu/selftune/, publications, and freely disseminated software. The project will provide educational and research experience opportunities for graduate and undegraduate students.
