An i/o optimal and scalable skyline query algorithm

  • Authors:

  • Yunjun Gao;Gencai Chen;Ling Chen;Chun Chen

  • Affiliations:

  • College of Computer Science, Zhejiang University, Hangzhou, P.R. China;College of Computer Science, Zhejiang University, Hangzhou, P.R. China;College of Computer Science, Zhejiang University, Hangzhou, P.R. China;College of Computer Science, Zhejiang University, Hangzhou, P.R. China

  • Venue:
  • BNCOD'06 Proceedings of the 23rd British National Conference on Databases, conference on Flexible and Efficient Information Handling
  • Year:
  • 2006

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Abstract

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.