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Continuous K-Nearest Neighbor Query for Moving Objects with Uncertain Velocity

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Abstract

One of the most important queries in spatio-temporal databases that aim at managing moving objects efficiently is the continuous K-nearest neighbor (CKNN) query. A CKNN query is to retrieve the K-nearest neighbors (KNNs) of a moving user at each time instant within a user-given time interval [t s , t e ]. In this paper, we investigate how to process a CKNN query efficiently. Different from the previous related works, our work relieves the past assumption, that an object moves with a fixed velocity, by allowing that the velocity of the object can vary within a known range. Due to the introduction of this uncertainty on the velocity of each object, processing a CKNN query becomes much more complicated. We will discuss the complications incurred by this uncertainty and propose a cost-effective P2 KNN algorithm to find the objects that could be the KNNs at each time instant within the given query time interval. Besides, a probability-based model is designed to quantify the possibility of each object being one of the KNNs. Comprehensive experiments demonstrate the efficiency and the effectiveness of the proposed approach.

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Acknowledgement

This work was supported by National Science Council of Taiwan (R.O.C.) under Grants NSC95-2221-E-006-206-MY3 and NSC95-2221-E-006-208.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Cheng-Kung University, Tainan, Taiwan, Republic of China

    Yuan-Ko Huang & Chiang Lee

  2. Department of Information Communication, Southern Taiwan University of Technology, Tainan, Taiwan, Republic of China

    Chao-Chun Chen

Authors
  1. Yuan-Ko Huang
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  2. Chao-Chun Chen
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  3. Chiang Lee
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Correspondence to Chiang Lee.

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Huang, YK., Chen, CC. & Lee, C. Continuous K-Nearest Neighbor Query for Moving Objects with Uncertain Velocity. Geoinformatica 13, 1–25 (2009). https://doi.org/10.1007/s10707-007-0041-0

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  • DOI: https://doi.org/10.1007/s10707-007-0041-0

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