Finding k-dominant Skyline cube based on sharing-strategy

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.