Scalable skyline computation using a balanced pivot selection technique

Jongwuk Lee, Seung Won Hwang

Research output: Contribution to journalArticlepeer-review

54 Citations (Scopus)


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

Original languageEnglish
Pages (from-to)1-21
Number of pages21
JournalInformation Systems
Issue number1
Publication statusPublished - 2014

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Hardware and Architecture


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