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Trajectory Mining

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Synonyms

Location sequence mining; Trajectory data analysis; Trajectory pattern mining

Definition

Trajectory mining is to analyze the trajectories collected from moving objects and discover the patterns including clustering (the grouping of similar trajectories), classification (classify the trajectories into different categories), anomaly and interesting location detection (identify the outliers and interesting locations in trajectories), and join (compute pairs of similar objects from two trajectory collections). Figure 1 illustrates the architectural context for mining trajectories (Zheng and Zhou, 2011). First, the trajectory data is collected from devices on moving objects by online or offline methods; second, the preprocessing includes data calibration; and third, trajectory mining is to discover the spatial, spatio-temporal and behavioral patterns from trajectories.

Trajectory Mining, Fig. 1
figure 1

Trajectory mining framework

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References

  • Chen Y, Patel JM (2009) Design and evaluation of trajectory join algorithms. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, Seattle, Washington, pp 266–275

    Google Scholar 

  • Chen L, Ozsu MT, Oria V (2005) Robust and fast similarity search for moving object trajectories. In: Proceedings of the 24th ACM SIGMOD international conference on management of data, Baltimore, Maryland, pp 491–502

    Google Scholar 

  • Ge Y, Xiong H, Tuzhilin A, Xiao K, Gruteser M, Pazzani M (2010a) An energy-efficient mobile recommender system. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, Washington, DC, pp 899–908

    Google Scholar 

  • Ge Y, Xiong H, Zhou Z-h, Ozdemir H, Yu J, Lee KC (2010b) Top-eye: top-k evolving trajectory outlier detection. In: Proceedings of the 19th ACM international conference on information and knowledge management, Toronto, Canada, pp 1733–1736

    Google Scholar 

  • Ghose A, Li B, Liu S (2015) Trajectory-based mobile advertising. In: Proceedings of the international conference on information systems, Madeira

    Google Scholar 

  • Giannotti F, Nanni M, Pinelli F, Pedreschi D (2007) Trajectory pattern mining. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, San Jose, CA, pp 330–339

    Chapter  Google Scholar 

  • Lee J-G, Han J, Whang K-Y (2007) Trajectory clustering: a partition-and-group framework. In: Proceedings of the 26th ACM SIGMOD international conference on management of data, Beijing, China, pp 593–604

    Google Scholar 

  • Lee J-G, Han J, Li X (2008a) Trajectory outlier detection: a partition-and-detect framework. In: Proceedings of the 24th IEEE international conference on data engineering, Cancún, Mexico, pp 140–149

    Google Scholar 

  • Lee J-G, Han J, Li X, Gonzalez H (2008b) Traclass: trajectory classification using hierarchical region-based and trajectory-based clustering. Proc VLDB Endow 1(1):1081–1094

    Article  Google Scholar 

  • Liu S, Liu Y, Ni LM, Fan J, Li M (2010) Towards mobility-based clustering. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, Washington, DC, pp 919–928

    Google Scholar 

  • Liu S, Kang L, Chen L, Ni L (2012a) Distributed incomplete pattern matching via a novel weighted bloom filter. In: Proceedings of the 32nd IEEE ICDCS international conference on distributed computing systems, Macau, China, pp 122–131

    Google Scholar 

  • Liu Y, Zhao Y, Chen L, Pei J, Han J (2012b) Mining frequent trajectory patterns for activity monitoring using radio frequency tag arrays. IEEE Trans Parallel Distrib Syst 23:2138–2149

    Article  Google Scholar 

  • Liu S, Wang S, Jayarajah K, Misra A, Krishnan R (2013a) Todmis: mining communities from trajectories. In: Proceedings of ACM the 22nd CIKM international conference on information and knowledge management, San Francisco, CA, pp 2109–2118

    Google Scholar 

  • Liu S, Yue Y, Krishnan R (2013b) Adaptive collective routing using gaussian process dynamic congestion models. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, Chicago, IL, pp 704–712

    Chapter  Google Scholar 

  • Liu C, Ge Y, Xiong H, Xiao K, Geng W, Perkins M (2014) Proactive workflow modeling by stochastic processes with application to healthcare operation and management. In: The 20th ACM SIGKDD international conference on knowledge discovery and data mining, New York City, NY

    Google Scholar 

  • Tang LA, Zheng Y, Yuan J, Han J, Leung A, Hung C-C, Peng W-C (2012) On discovery of traveling companions from streaming trajectories. In: Proceedings of the 28th IEEE ICDE international conference on data engineering, Washington, DC, pp 186–197

    Google Scholar 

  • Vlachos M, Gunopulos D, Kollios G (2012) Discovering similar multidimensional trajectories. In: Proceedings of the 18th IEEE ICDE international conference on data engineering, Washington, DC, pp 673–684

    Google Scholar 

  • Zheng Y, Zhou X (2011) Computing with spatial trajectories. Springer, New York

    Book  Google Scholar 

  • Zheng Y, Zhang L, Xie X, Ma W-Y (2009) Mining interesting locations and travel sequences from gps trajectories. In: Proceedings of the 18th international conference on world wide web, Madrid, Spain, pp 791–800

    Chapter  Google Scholar 

  • Zheng K, Zheng Y, Yuan NJ, Shang S, Zhou X (2014) Online discovery of gathering patterns over trajectories. IEEE Trans Knowl Discov Data Eng 26:1974–1988

    Article  Google Scholar 

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Acknowledgements

This research was supported by National Basic Research Program of China (973 Program), 2015CB352400 and 2012CB316400; Basic Research Program of Shenzhen, JCYJ20140610152828686; National Natural Science Foundation of China (Grant No. 61572488, 61303160 and U1401258); and Russian Science Foundation under Grant No. 15-11-10032. Siyuan Liu would also acknowledge Google Faculty Research Award.

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Authors and Affiliations

  1. Smeal College of Business, The Pennsylvania State University, University Park, TX, USA

    Siyuan Liu

  2. Chinese Academy of Sciences, Beijing, China

    Shuhui Wang

  3. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Qiang Qu

Authors
  1. Siyuan Liu
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  2. Shuhui Wang
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  3. Qiang Qu
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Corresponding author

Correspondence to Siyuan Liu .

Editor information

Editors and Affiliations

  1. Minneapolis, Minnesota, USA

    Shashi Shekhar

  2. Rutgers Business School, Newark, New Jersey, USA

    Hui Xiong

  3. The University of Iowa Tippie College of Business, Iowa City, Iowa, USA

    Xun Zhou

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© 2016 Springer International Publishing Switzerland

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Liu, S., Wang, S., Qu, Q. (2016). Trajectory Mining. In: Shekhar, S., Xiong, H., Zhou, X. (eds) Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-23519-6_1576-2

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  • DOI: https://doi.org/10.1007/978-3-319-23519-6_1576-2

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  • Publisher Name: Springer, Cham

  • Online ISBN: 978-3-319-23519-6

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Chapter history

  1. Latest

    Trajectory Mining
    Published:
    06 October 2016

    DOI: https://doi.org/10.1007/978-3-319-23519-6_1576-2

  2. Original

    Trajectory Mining
    Published:
    31 May 2016

    DOI: https://doi.org/10.1007/978-3-319-23519-6_1576-1

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