Selecting skyline stars over uncertain databases: Semantics and refining methods in the evidence theory setting

摘要

In recent years, a great attention has been paid to skyline computation over uncertain data. In this paper, we study how to conduct advanced skyline analysis over uncertain databases where uncertainty is modeled thanks to the evidence theory (a.k.a., belief functions theory). We particularly tackle an important issue, namely the skyline stars (denoted by SKY2) over the evidential data. This kind of skyline aims at retrieving the best evidential skyline objects (or the stars). Efficient algorithms have been developed to compute the SKY2. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approaches that considerably refine the huge skyline. In addition, the conducted experiments have shown that our algorithms significantly outperform the basic skyline algorithms in terms of CPU and memory costs. (C) 2017 Elsevier B.V. All rights reserved.