MSSQ: Manhattan spatial skyline queries

Wanbin Son, Seung Won Hwang, Hee Kap Ahn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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, L 2 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 L 1 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.

Original languageEnglish
Title of host publicationAdvances in Spatial and Temporal Databases - 12th International Symposium, SSTD 2011, Proceedings
Pages313-329
Number of pages17
DOIs
Publication statusPublished - 2011 Sep 19
Event12th International Symposium on Advances in Spatial and Temporal Databases, SSTD 2011 - Minneapolis, MN, United States
Duration: 2011 Aug 242011 Aug 26

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6849 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other12th International Symposium on Advances in Spatial and Temporal Databases, SSTD 2011
CountryUnited States
CityMinneapolis, MN
Period11/8/2411/8/26

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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    Son, W., Hwang, S. W., & Ahn, H. K. (2011). MSSQ: Manhattan spatial skyline queries. In Advances in Spatial and Temporal Databases - 12th International Symposium, SSTD 2011, Proceedings (pp. 313-329). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6849 LNCS). https://doi.org/10.1007/978-3-642-22922-0_19