Preference SQL - Design, Implementation, Experiences

Abstract

Current search engines can hardly cope adequately with complex preferences. The biggest problem of search engines directly implemented with standard SQL is that SQL does not directly understand the notion of preferences. Preference SQL extends standard SQL by a preference model based on strict partial orders, where preference queries behave like soft selection constraints. A variety of built-in base preference types and the powerful Pareto accumulation operator to construct complex preferences, combined with the adherence to declarative SQL programming style, guarantees great programming productivity. The current Preference SQL optimizer does an efficient re-writing into standard SQL, including a high-level implementation of the skyline operator for Pareto-optimal sets. This pre-processor approach enables a seamless application integration, making reference SQL available on a broad variety of SQL platforms including IBM DB2, Oracle, Microsoft SQL Server and Sybase. The benefits of Preference SQL technology comprise cooperative query answering and smart customer advice, leading to a higher e-customer satisfaction and shorter development times of personalized search engines for the e-service provider. We report experiences with practical applications ranging from m-commerce and comparison shopping to a large-scale performance test with real data. Several search engines of commercial B2C portals are powered by Preference SQL