Abstract Skyline query plays an important role in multi-decision and data mining. However, with the growth of data dimension, Skyline set becomes very large. Skyline representative point query is studied to overcome this shortcoming. A new evaluation function is proposed to improve the score-computing of Skyline points so as to select k representative Skyline points. A dynamic programming based algorithm(DPBA) in two-dimensional space is presented. The Eulerian distance between representative point and non-representative point is determined by the cover circle. k representative points are got by computing the evaluation function iteratively. In high-dimensional space, an approximate solution based on aR-tree index is proposed to solve the NP-hard problem. The index tree is traversed to judge whether it is dominated by the candidate Skyline sets. If it is dominated, it should be pruned to reduce the computation cost. The experiments of synthetic and real data show that the proposed algorithms are effective and efficient.
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2015, Vol. 28 
