k-dominant and Extended k-dominant Skyline Computation by Using Statistics
Journal Title: International Journal on Computer Science and Engineering - Year 2010, Vol 2, Issue 5
Abstract
Skyline queries have recently attracted a lot of attention for its intuitive query formulation. It can act as a filter to discard ub-optimal objects. However, a major drawback of skyline is that, in datasets with many dimensions, the number of skyline objects becomes large and no longer offer any interesting nsights. To solve the problem, k-dominant skyline queries have been introduced, which can reduce the number of skyline objects by relaxing the definition of the dominance. However, sometimes, a kdominant skyline query may retrieve too few objects to analyze. This paper addresses the problem of -dominant skyline for high dimensional dataset. In addition, we extend the notion of k-domination by defining extended k-dominant skyline, which retrieves neither too many nor too few objects. We propose algorithms for k-dominant and xtended kdominant skyline computation. An extensive erformance evaluation using both real and synthetic datasets demonstrated that our proposed methods are efficient and scalable.
Authors and Affiliations
Md. Anisuzzaman Siddique , Yasuhiko Morimoto
Keywords
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