MSSQ: Manhattan Spatial Skyline Queries

  • Wanbin Son
  • Seung-won Hwang
  • Hee-Kap Ahn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6849)

Abstract

Skyline queries have gained attention lately for supporting effective retrieval over massive spatial data. While efficient algorithms have been studied for spatial skyline queries using Euclidean distance, or, L2 norm, these algorithms are (1) still quite computationally intensive and (2) unaware of the road constraints. Our goal is to develop a more efficient algorithm for L1 norm, also known as Manhattan distance, which closely reflects road network distance for metro areas with well-connected road networks. Towards this goal, we present a simple and efficient algorithm which, given a set P of data points and a set Q of query points in the plane, returns the set of spatial skyline points in just O(|P|log|P|) time, assuming that |Q| ≤ |P|. This is significantly lower in complexity than the best known method. In addition to efficiency and applicability, our proposed algorithm has another desirable property of independent computation and extensibility to L ∞  norm, which naturally invites parallelism and widens applicability. Our extensive empirical results suggest that our algorithm outperforms the state-of-the-art approaches by orders of magnitude.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Wanbin Son
    • 1
  • Seung-won Hwang
    • 1
  • Hee-Kap Ahn
    • 1
  1. 1.Pohang University of Science and TechnologyKorea

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