Current search engines can hardly cope adequately with fuzzy predicates defined by complex preferences. The biggest problem of search engines implemented with standard SQL is that SQL does not directly understand the notion of preferences. Preference SQL extends SQL by a preference model based on strict partial orders (presented in more detail in the companion paper [Kie02]), where preference queries behave like soft selection constraints. Several built-in base preference types and the powerful Pareto operator, combined with the adherence to declarative SQL programming style, guarantees great programming productivity. The 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 Preference SQL available on all major SQL platforms. Several commercial B2C portals are powered by Preference SQL. Its benefits comprise cooperative query answering and smart customer advice, leading to higher ecustomer satisfaction and shorter development times of personalized search engines. We report practical experiences ranging from m-commerce and comparison shopping to a large-scale performance test for a job portal.
Reference REF Introduced Preference SQL which extends SQL with some built-in base preference constructors and a pareto accumulation and a prioritized accumulation constructors.
Preference SQL - Design, Implementation, Experiences
VLDB
Computer Science