Unifying Qualitative and Quantitative Database Preferences to Enhance Query Personalization

Query personalization can be an effective technique in dealing with the data scalability challenge, primarily from the human point of view, i.e., making big data easier to use. In order to customize their query results, users need to express their preferences in a simple and user-friendly manner. In this paper, we present a graph-based theoretical framework and a prototype system that unify qualitative and quantitative preferences, while eliminating their disadvantages. Our integrated system allows for (1) the specification of database preferences and the creation of user preference profiles in a user-friendly manner, (2) the manipulation of preferences of individuals or groups of users and (3) total ordering of the tuples in the database, matching both qualitative and quantitative preferences, hence significantly increasing the number of tuples covered by the user preferences. We confirmed the latter experimentally by comparing our preference selection algorithm with Fagin's TA algorithm.