Scalable skyline computation using a balanced pivot selection technique

摘要: Skyline queries have recently received considerable attention as an alternative decision-making operator in the database community. The conventional skyline algorithms primarily focused on optimizing dominance of points order to remove non-skyline efficiently possible, but neglected take into account incomparability bypass unnecessary comparisons. To design a scalable algorithm, we first analyze cost model that copes with both and incomparability, develop novel technique select cost-optimal point, called pivot minimizes number comparisons point-based space partitioning. We then implement proposed point selection existing sorting- partitioning-based algorithms. For insertions/deletions, also discuss how maintain current using skytree, derived from recursive Furthermore, efficient greedy algorithm for k representative skytree. Experimental results demonstrate are significantly faster than state-of-the-art