Composition and Efficient Evaluation of Context-Aware Preference Queries

Abstract. This paper presents a modular approach to context-aware preference query composition based on a novel kind of preference generator. We introduce a constructive model to generate preference terms within the Preference SQL framework. Given several sources for preference related knowledge like explicit user input, information extracted from a preference repository, domain-specific application knowledge, location-based sensor data, or web service feeds for weather data our preference generator can compile a user search request into one rather complex context-aware Preference SQL query. Choosing as use case a commercial e-business platform for outdoor activities, we demonstrate how such queries despite the power and complexity of this approach can be evaluated efficiently on a practical data set.