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Towards Efficient Path Skyline Computation in Bicriteria Networks

  • Dian Ouyang
  • Long Yuan
  • Fan Zhang
  • Lu Qin
  • Xuemin Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)

Abstract

Path skyline query is a fundamental problem in bicriteria network analysis and is widely applied in a variety of applications. Given a source s and a destination t in a bicriteria network G, path skyline query aims to identify all the skyline paths from s to t in G. In the literature, \(\mathsf {PSQ}\) is a fundamental algorithm for path skyline query and is also used as a building block for the afterwards proposed algorithms. In \(\mathsf {PSQ}\), a key operation is to record the skyline paths from s to v for each node v that is possible on the skyline paths from s to t. However, to obtain the skyline paths for v, \(\mathsf {PSQ}\) has to maintain other paths that are not skyline paths for v, which makes \(\mathsf {PSQ}\) inefficient. Motivated by this, in this paper, we propose a new algorithm \(\mathsf {PSQ^+}\) for the path skyline query. By adopting an ordered path exploring strategy, our algorithm can totally avoid the fruitless path maintenance problem in \(\mathsf {PSQ}\). We evaluate our proposed algorithm on real networks and the experimental results demonstrate the efficiency of our proposed algorithm. Besides, the experimental results also demonstrate the algorithm that uses \(\mathsf {PSQ}\) as a building block for the path skyline query can achieve a significant performance improvement after we substitute \(\mathsf {PSQ^+}\) for \(\mathsf {PSQ}\).

Notes

Acknowledgments

Long Yuan is supported by Huawei YBN2017100007. Fan Zhang is supported by Huawei YBN2017100007. Lu Qin is supported by ARC DP160101513. Xuemin Lin is supported by NSFC 61672235, ARC DP170101628, DP180103096 and Huawei YBN2017100007.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Dian Ouyang
    • 1
  • Long Yuan
    • 2
  • Fan Zhang
    • 2
  • Lu Qin
    • 1
  • Xuemin Lin
    • 2
  1. 1.Centre for Artificial IntelligenceUniversity of Technology SydneySydneyAustralia
  2. 2.The University of New South WalesSydneyAustralia

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