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On discovering motifs and frequent patterns in spatial trajectories with discrete Fréchet distance

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Abstract

The discrete Fréchet distance (DFD) captures perceptual and geographical similarity between two trajectories. It has been successfully adopted in a multitude of applications, such as signature and handwriting recognition, computer graphics, as well as geographic applications. Spatial applications, e.g., sports analysis, traffic analysis, etc. require discovering similar subtrajectories within a single trajectory or across multiple trajectories. In this paper, we adopt DFD as the similarity measure, and study two representative trajectory analysis problems, namely, motif discovery and frequent pattern discovery. Due to the time complexity of DFD, these tasks are computationally challenging. We address that challenge with a suite of novel lower bound functions and a grouping-based solution. Our techniques apply directly when the analysis tasks are defined within the same or across multiple trajectories. An extensive empirical study on real trajectory datasets reveals that our approaches are 3 orders of magnitude faster than baseline solutions.

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Notes

  1. Intel architectures optimization manual: http://intel.ly/2lgN4rc

  2. http://chorochronos.datastories.org/

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Acknowledgments

Bo Tang was supported by the National Science Foundation of China (NSFC No. 61802163), the Education Department of Guangdong (Grant No. 2020KZDZX1184, 2020ZDZX3043), the Guangdong Provincial Key Laboratory (Grant No. 2020B121201001), the PCL Future Greater-Bay Area Network Facilities for Larg scale Experiments and Applications (LZC0019). Man Lung Yiu was supported by GRF 152050/19E from the Hong Kong RGC. Kyriakos Mouratidis was supported by by the Singapore Management University Lee Kong Chian Fellowship.

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Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Bo Tang

  2. Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

    Man Lung Yiu & Jiahao Zhang

  3. School of Computing and Information Systems, Singapore Management University, Singapore, Singapore

    Kyriakos Mouratidis

  4. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia

    Kai Wang

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  1. Bo Tang
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  2. Man Lung Yiu
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  3. Kyriakos Mouratidis
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  4. Jiahao Zhang
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  5. Kai Wang
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Correspondence to Bo Tang.

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Tang, B., Yiu, M.L., Mouratidis, K. et al. On discovering motifs and frequent patterns in spatial trajectories with discrete Fréchet distance. Geoinformatica 26, 29–66 (2022). https://doi.org/10.1007/s10707-021-00438-x

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