Preference constructors for deeply personalized database queries

Deep personalization of database queries requires a semantically rich, easy to handle and flexible preference model. Building on preferences as strict partial orders we provide a variety of intuitive and customizable base preference constructors for numerical and categorical data. For complex constructors we introduce the notion of "substitutable values" (SV-semantics). Preferences with SV-semantics solve major open problems with Pareto and prioritized preferences. Known laws from preference relational algebra remain valid under SV-semantics. These powerful modeling capabilities even contribute to improve efficient preference query evaluation. Moreover, for the first time we point out a semantic-guided way to cope with the infamous flooding effect of query engines. Performing a series of test queries on sample data from an e-procurement application, we provide evidence that the flooding problem comes under control for deeply personalized database queries