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Efficient Top-k Spatial Distance Joins

  • Shuyao Qi
  • Panagiotis Bouros
  • Nikos Mamoulis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8098)

Abstract

Consider two sets of spatial objects R and S, where each object is assigned a score (e.g., ranking). Given a spatial distance threshold ε and an integer k, the top-k spatial distance join (k- SDJ) returns the k pairs of objects, which have the highest combined score (based on an aggregate function γ) among all object pairs in R×S which have spatial distance at most ε. Despite the practical application value of this query, it has not received adequate attention in the past. In this paper, we fill this gap by proposing methods that utilize both location and score information from the objects, enabling top-k join computation by accessing a limited number of objects. Extensive experiments demonstrate that a technique which accesses blocks of data from R and S ordered by the object scores and then joins them using an aR-tree based module performs best in practice and outperforms alternative solutions by a wide margin.

Keywords

Range Query Spatial Distance Priority Queue Seed Point Spatial Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Shuyao Qi
    • 1
  • Panagiotis Bouros
    • 2
  • Nikos Mamoulis
    • 1
  1. 1.Department of Computer ScienceThe University of Hong KongHonk Kong
  2. 2.Department of Computer ScienceHumboldt-Universität zu BerlinGermany

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