Finding k-dominant Skyline cube based on sharing-strategy
Leigang Dong, Xiaowei Cui, Zhenfu Wang, Shu-wei Cheng · 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery · 2010
K-dominant skyline query has been proposed as an important operator for multi-criteria decision making, data mining and so on, this technology can reduce the large result sets of skyline query in high dimensional space. In this paper, a new concept was firstly proposed: k-dominant Skyline cube, which consists of all the k-dominant skylines. Although existing algorithms can compute every k-dominant skyline, they lead to much repeat work because of no sharing result. We develop two computation sharing strategies—ASCEND sharing strategy and DESCEND sharing strategy. Based on these two sharing strategies, two novel algorithms—BUA (Bottom-Up Algorithm) and UBA (Up-Bottom Algorithm) are proposed to compute k-dominant skyline cube. Furthermore, detailed theoretical analyses and extensive experiments demonstrate that our algorithms are both efficient and effective.