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Approximate aggregate nearest neighbor search on moving objects trajectories

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Abstract

Aggregate nearest neighbor (ANN) search retrieves for two spatial datasets T and Q, segment(s) of one or more trajectories from the set T having minimum aggregate distance to points in Q. When interacting with large amounts of trajectories, this process would be very time-consuming due to consecutive page loads. An approximate method for finding segments with minimum aggregate distance is proposed which can improve the response time. In order to index large volumes of trajectories, scalable and efficient trajectory index (SETI) structure is used. But some refinements are provided to temporal index of SETI to improve the performance of proposed method. The experiments were performed with different number of query points and percentages of dataset. It is shown that proposed method besides having an acceptable precision, can reduce the computation time significantly. It is also shown that the main fraction of search time among load time, ANN and computing convex and centroid, is related to ANN.

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Authors and Affiliations

  1. MODB Lab., School of Computer Engineering, Iran University of Science and Technology, Tehran, 1684613114, Iran

    Mohammad Reza Abbasifard, Hassan Naderi, Zohreh Fallahnejad & Omid Isfahani Alamdari

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  1. Mohammad Reza Abbasifard
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  2. Hassan Naderi
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  3. Zohreh Fallahnejad
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  4. Omid Isfahani Alamdari
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Correspondence to Hassan Naderi.

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Abbasifard, M.R., Naderi, H., Fallahnejad, Z. et al. Approximate aggregate nearest neighbor search on moving objects trajectories. J. Cent. South Univ. 22, 4246–4253 (2015). https://doi.org/10.1007/s11771-015-2973-0

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  • DOI: https://doi.org/10.1007/s11771-015-2973-0

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