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Semantic Understanding of Spatial Trajectories

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Advances in Spatial and Temporal Databases (SSTD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10411))

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Abstract

The advances in location-acquisition technologies and the prevalence of location-based services have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles, and animals. Such trajectories offer us unprecedented information to understand moving objects and locations that could benefit a broad range of applications.

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Acknowledgements

This work was supported in part by NSF awards #1618448, #1652525, #1639150, and #1544455. The views and conclusions contained in this paper are those of the author and should not be interpreted as representing any funding agencies.

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Authors and Affiliations

  1. College of Information Sciences and Technology, Pennsylvania State University, University Park, PA, USA

    Zhenhui Li

Authors
  1. Zhenhui Li
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Correspondence to Zhenhui Li .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, Heidelberg University, Heidelberg, Germany

    Michael Gertz

  2. George Mason University, Fairfax, Virginia, USA

    Matthias Renz

  3. University of Queensland, Brisbane, Queensland, Australia

    Xiaofang Zhou

  4. ESRI, University of Minnesota, Minneapolis, Minnesota, USA

    Erik Hoel

  5. Auburn University, Auburn, Alabama, USA

    Wei-Shinn Ku

  6. Free University of Berlin, Dahlem, Berlin, Germany

    Agnes Voisard

  7. Microsoft, Redmond, Washington, USA

    Chengyang Zhang

  8. California State University, Sacramento, California, USA

    Haiquan Chen

  9. LinkedIn, Sunnyvale, California, USA

    Liang Tang

  10. University of North Texas, Denton, Texas, USA

    Yan Huang

  11. Virginia Tech, Falls Church, Virginia, USA

    Chang-Tien Lu

  12. Oracle, Redwood Shores, California, USA

    Siva Ravada

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Li, Z. (2017). Semantic Understanding of Spatial Trajectories. In: Gertz, M., et al. Advances in Spatial and Temporal Databases. SSTD 2017. Lecture Notes in Computer Science(), vol 10411. Springer, Cham. https://doi.org/10.1007/978-3-319-64367-0_24

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  • DOI: https://doi.org/10.1007/978-3-319-64367-0_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64366-3

  • Online ISBN: 978-3-319-64367-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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