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The direction-constrained k nearest neighbor query

Dealing with spatio-directional objects

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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.

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Notes

  1. PanoGenerator, iOS (https://itunes.apple.com/us/app/panogenerator/id855946588).

  2. The minimum and the maximum values are the same for a circulant dimension.

  3. ⊃ c , ⊇ c , and ∋ c are similarly defined.

  4. Arithmetically, (θ 1± c θ 2) is identical to ((θ 1±θ 2)∓360⋅x), where x is a non-negative integer for the resulting direction value to be between 0 and 360.

  5. To replace two neighboring odd R-trees, we need a structure using 2(2h−1)N/B space which is too expensive.

  6. Under the uniform direction assumption. The justification for the assumption can be found at the end of Section 5.1.1 and Fig. 10.

  7. You can download the SD spatio-directional object dataset at http://islab.kaist.ac.kr/mjlee/SDset

  8. http://foursquare.com

  9. http://geonames.usgs.gov/

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Acknowledgments

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

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Authors and Affiliations

  1. The Chongqing Liangjiang KAIST International Program, Chongqing University of Technology, Chongqing, China

    Chin-Wan Chung

  2. School of Computing, KAIST, Daejeon, Republic of Korea

    Min-Joong Lee, SangYeon Kim, Ha-Myung Park, Sunghee Choi & Chin-Wan Chung

  3. School of Computing Science, Simon Fraser University, Burnaby, BC, Canada

    Dong-Wan Choi

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  1. Min-Joong Lee
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Correspondence to Chin-Wan Chung.

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Lee, MJ., Choi, DW., Kim, S. et al. The direction-constrained k nearest neighbor query. Geoinformatica 20, 471–502 (2016). https://doi.org/10.1007/s10707-016-0245-2

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