An I/O Optimal and Scalable Skyline Query Algorithm

  • Yunjun Gao
  • Gencai Chen
  • Ling Chen
  • Chun Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4042)


Given a set of d-dimensional points, skyline query returns the points that are not dominated by any other point on all dimensions. Currently, BBS (branch-and-bound skyline) is the most efficient skyline processing method over static data in a centralized setting. Although BBS has some desirable features (e.g., I/O optimal and flexibility), it requires large main-memory consumption. In this paper, we present an improved skyline computation algorithm based on best-first nearest neighbor search, called IBBS, which captures the optimal I/O and less memory space (i.e., IBBS visits and stores only those entries that contribute to the final skyline). Its core enables several effective pruning strategies to discard non-qualifying entries. Extensive experimental evaluations show that IBBS outperforms BBS in both scalability and efficiency for most cases, especially in low dimensions.


Near Neighbor Skyline Query Pruning Strategy Skyline Point Skyline Computation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yunjun Gao
    • 1
  • Gencai Chen
    • 1
  • Ling Chen
    • 1
  • Chun Chen
    • 1
  1. 1.College of Computer ScienceZhejiang UniversityHangzhouP.R. China

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