Discovering Group Skylines with Constraints by Early Candidate Pruning

Abstract

Skyline query has been an important issue in the database community. Many applications nowadays request the skyline after grouping tuples, such as fantasy sports, so that the group skyline problem becomes the research focus. Most previous algorithms intended to quickly sift through the numerous combinations but fail to address the problem of constraints. In practice, nearly all groupings are specified with constrains, which demand solutions of constrained group skyline. In this paper, we propose an algorithm called CGSky to efficiently solve the problem. CGSky utilizes a pre-processing method to exclude the unnecessary tuples and generate candidate groups incrementally. A pruning mechanism is devised in the algorithm to prevent non-qualifying candidates from the skyline computation. Our experimental results show that CGSky improves an order of magnitude over previous algorithms in average. It also shows that CGSky has good scale-up capability on different data distributions.