Restricting skyline sizes using weak Pareto dominance

Abstract

Skyline queries have recently received a lot of attention due to their intuitive query formulation: users can state preferences with respect to several attributes. Unlike numerical or score-based preferences, preferences over discrete value domains do not show an inherent total order, but have to rely on partial orders as stated by the user. In such orders typically many object values are incomparable, increasing the size of skyline sets significantly, and making their computation expensive. In this paper we explore how to enable interactive tasks like query refinement or relevance feedback by providing interesting subsets of the full Pareto skyline, which give users a good overview over the skyline. To be practical these subsets have to be small, efficient to compute, suitable for higher numbers of query predicates, and representative. The key to improved performance and reduced result set sizes is the relaxation of Pareto semantics to the concept of weak Pareto dominance. We argue that this relaxation yields intuitive results and show how it opens up the use of efficient and scalable query processing algorithms. We first derive the complete skyline subset given by weak Pareto dominance called ‘restricted skyline’ and then considering the individual performance of objects limit this further to a subset called ‘focused skyline’. Assessing the practical impact our experiments show that our approach indeed leads to lean result set sizes and outperforms Pareto skyline computations by up to two orders of magnitude.