MP2R: A Human-Centric Skyline Relaxation Approach

Abstract

Skyline queries have gained much attention in the last decade and are proved to be valuable for multi-criteria decision making. They are based on the concept of Pareto dominance. When computing the skyline, two scenarios may occur: either (i) a huge number of skyline which is less informative for the user or (ii) a small number of returned objects which could be insufficient for the user needs. In this paper, we tackle the second problem and propose an approach to deal with it. The idea consists in making the skyline more permissive by adding points that strictly speaking do not belong to it, but are close to belonging to it. A new fuzzy variant of dominance relationship is then introduced. Furthermore, an efficient algorithm to compute the relaxed skyline is proposed. Extensive experiments are conducted to demonstrate the effectiveness of our approach and the performance of the proposed algorithm