An outranking-based approach for skyline refinement
Mohamed Haddache, Djamal Belkasmi, Allel Hadjali, Hamid Azzoune · 2016
Recently, the study of the skyline and its variants has received considerable attention in various applications that require decision making and personalized services. The skyline is essentially a set of the most interesting (not dominated) tuples in the queried database. They are based on the concept of Pareto dominance. The skyline computation may lead to two scenarios: either (i) a huge number of skyline tuples which is less informative for the user or (ii) a small number of skyline tuples which could be insufficient for the user needs. In this paper, we focus on the first problem and propose an approach to deal with it. The idea is to rank the skyline using a method borrowed from the outranking field, then select the top-k tuples. Then, we develop an efficient algorithm to compute the refined skyline. Experimental study shows the efficiency and the effectiveness of our approach to reduce skyline size.