An algebraic compression framework for query results

Abstract

Decision-support applications in emerging environments require that SQL query results or intermediate results be shipped to clients for further analysis and presentation. These clients may use low bandwidth connections or have severe storage restrictions. Consequently, there is a need to compress the results of a query for efficient transfer and client-side access. This paper explores a variety of techniques that address this issue. Instead of using a fixed method, we choose a combination of compression methods that use statistical and semantic information of the query results to enhance the effect of compression. To represent such a combination, we present a framework of "compression plans" formed by composing primitive compression operators. We also present optimization algorithms that enumerate valid compression plans and choose an optimal plan. Our experiments show that our techniques achieve significant performance improvement over standard compression tools like WinZip.