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)


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
Number of pages17
Publication statusPublished - 2011
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


Other12th International Symposium on Advances in Spatial and Temporal Databases, SSTD 2011
CountryUnited States
CityMinneapolis, MN

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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