BFPkNN: An Efficient k-Nearest-Neighbor Search Algorithm for Historical Moving Object Trajectories

  • Yunjun Gao
  • Chun Li
  • Gencai Chen
  • Ling Chen
  • Xianta Jiang
  • Chun Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4243)


This paper studies k-nearest-neighbor (kNN) search on R-tree-based structures storing historical information about trajectories. We develop BFPkNN, an efficient best-first based algorithm for handling kNN search with arbitrary values of k, which is I/O optimal, i.e., it performs a single access only to those qualifying nodes that may contain the final result. Furthermore, in order to save memory space consumption and reduce CPU overhead further, several effective pruning heuristics are also proposed. Finally, extensive experiments with synthetic and real datasets show that BFPkNN outperforms its competitor significantly in both efficiency and scalability in all cases.


Leaf Node Query Point Query Time Query Object Neighbor Query 
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 2006

Authors and Affiliations

  • Yunjun Gao
    • 1
  • Chun Li
    • 1
  • Gencai Chen
    • 1
  • Ling Chen
    • 1
  • Xianta Jiang
    • 1
  • Chun Chen
    • 1
  1. 1.College of Computer ScienceZhejiang UniversityHangzhouP.R. China

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