Condition learning from user preferences

The utility of preferences within the database domain is widely accepted. Preferences provide an effective means for query personalization and information filtering. Nevertheless, two preference approaches - qualitative and quantitative ones - do still compete. In this paper, we contribute to the bridging of both approaches and compare their expressive power and different usage scenarios. In order to combine qualitative and quantitative preferences, mappings are introduced and discussed, which transform a query from one approach into its counter-part. We consider Chomicki's preference formulas and as a quantitative approach our CQQL approach that extends the relational calculus with proximity predicates. In order to facilitate query formulation for the user, we extend the CQQL approach to condition learning. That is, user-defined preferences amongst database objects serve as input to learn logical conditions within a CQQL query. Hereby, we can support the user in the cognitively demanding task of query formulation.