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Algorithms for constrained k-nearest neighbor queries over moving object trajectories

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Abstract

An important query for spatio-temporal databases is to find nearest trajectories of moving objects. Existing work on this topic focuses on the closest trajectories in the whole data space. In this paper, we introduce and solve constrained k-nearest neighbor (CkNN) queries and historical continuous CkNN (HCCkNN) queries on R-tree-like structures storing historical information about moving object trajectories. Given a trajectory set D, a query object (point or trajectory) q, a temporal extent T, and a constrained region CR, (i) a CkNN query over trajectories retrieves from D within T, the k (≥ 1) trajectories that lie closest to q and intersect (or are enclosed by) CR; and (ii) an HCCkNN query on trajectories retrieves the constrained k nearest neighbors (CkNNs) of q at any time instance of T. We propose a suite of algorithms for processing CkNN queries and HCCkNN queries respectively, with different properties and advantages. In particular, we thoroughly investigate two types of CkNN queries, i.e., CkNNP and CkNNT, which are defined with respect to stationary query points and moving query trajectories, respectively; and two types of HCCkNN queries, namely, HCCkNNP and HCCkNNT, which are continuous counterparts of CkNNP and CkNNT, respectively. Our methods utilize an existing data-partitioning index for trajectory data (i.e., TB-tree) to achieve low I/O and CPU cost. Extensive experiments with both real and synthetic datasets demonstrate the performance of the proposed algorithms in terms of efficiency and scalability.

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Notes

  1. A preliminary work has been published in DASFAA’08 as a short paper [14], in which the concept of constrained kNN (CkNN) search over moving objects trajectories has been introduced. However, due to the space limitation, we only managed to present the basic idea, but not the details, of CkNN query processing. In addition, the concept of historical continuous constrained kNN search on moving objects trajectories has not been defined in that work.

  2. The URL of the R-tree Portal is http://www.rtreeportal.org/.

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Acknowledgements

We would like to thank Elias Frentzos for his useful feedback on the source-codes of the proposed algorithms in [10, 11]. We also would like to express our gratitude to some anonymous reviewers, for giving valuable and helpful comments to improve the technical quality and presentation of this paper.

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Authors and Affiliations

  1. School of Information Systems, Singapore Management University, 80 Stamford Road, Singapore, 178902, Singapore

    Yunjun Gao & Baihua Zheng

  2. College of Computer Science, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, People’s Republic of China

    Yunjun Gao & Gencai Chen

  3. Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong

    Qing Li

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  1. Yunjun Gao
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  2. Baihua Zheng
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  3. Gencai Chen
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  4. Qing Li
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Correspondence to Yunjun Gao.

Additional information

This work is an extended version of [14].

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Gao, Y., Zheng, B., Chen, G. et al. Algorithms for constrained k-nearest neighbor queries over moving object trajectories. Geoinformatica 14, 241–276 (2010). https://doi.org/10.1007/s10707-009-0084-5

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