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GeoInformatica

, Volume 20, Issue 3, pp 471–502 | Cite as

The direction-constrained k nearest neighbor query

Dealing with spatio-directional objects
  • Min-Joong Lee
  • Dong-Wan Choi
  • SangYeon Kim
  • Ha-Myung Park
  • Sunghee Choi
  • Chin-Wan ChungEmail author
Article

Abstract

Finding k nearest neighbor objects in spatial databases is a fundamental problem in many geospatial systems and the direction is one of the key features of a spatial object. Moreover, the recent tremendous growth of sensor technologies in mobile devices produces an enormous amount of spatio-directional (i.e., spatially and directionally encoded) objects such as photos. Therefore, an efficient and proper utilization of the direction feature is a new challenge. Inspired by this issue and the traditional k nearest neighbor search problem, we devise a new type of query, called the direction-constrained k nearest neighbor (DCkNN) query. The DCkNN query finds k nearest neighbors from the location of the query such that the direction of each neighbor is in a certain range from the direction of the query. We develop a new index structure called MULTI, to efficiently answer the DCkNN query with two novel index access algorithms based on the cost analysis. Furthermore, our problem and solution can be generalized to deal with spatio-circulant dimensional (such as a direction and circulant periods of time such as an hour, a day, and a week) objects. Experimental results show that our proposed index structure and access algorithms outperform two adapted algorithms from existing kNN algorithms.

Keywords

k nearest neighbor search R-tree Spatial and directional object 

Notes

Acknowledgments

This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract UD140022PD, Korea.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Min-Joong Lee
    • 2
  • Dong-Wan Choi
    • 3
  • SangYeon Kim
    • 2
  • Ha-Myung Park
    • 2
  • Sunghee Choi
    • 2
  • Chin-Wan Chung
    • 1
    • 2
    Email author
  1. 1.The Chongqing Liangjiang KAIST International ProgramChongqing University of TechnologyChongqingChina
  2. 2.School of ComputingKAISTDaejeonRepublic of Korea
  3. 3.School of Computing ScienceSimon Fraser UniversityBurnabyCanada

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