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Mining Continuous Activity Patterns from Animal Trajectory Data

  • Yuwei Wang
  • Ze Luo
  • Baoping Yan
  • John Takekawa
  • Diann Prosser
  • Scott Newman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8933)

Abstract

The increasing availability of animal tracking data brings us opportunities and challenges to intuitively understand the mechanisms of animal activities. In this paper, we aim to discover animal movement patterns from animal trajectory data. In particular, we propose a notion of continuous activity pattern as the concise representation of underlying similar spatio-temporal movements, and develop an extension and refinement framework to discover the patterns. We first preprocess the trajectories into significant semantic locations with time property. Then, we apply a projection-based approach to generate candidate patterns and refine them to generate true patterns. A sequence graph structure and a simple and effective processing strategy is further developed to reduce the computational overhead. The proposed approaches are extensively validated on both real GPS datasets and large synthetic datasets.

Keywords

movement patterns continuous activity patterns 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yuwei Wang
    • 1
    • 2
  • Ze Luo
    • 1
  • Baoping Yan
    • 1
  • John Takekawa
    • 3
  • Diann Prosser
    • 4
  • Scott Newman
    • 5
  1. 1.Computer Network Information CenterChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.US Geological SurveyWestern Ecological Research CenterUSA
  4. 4.US Geological SurveyPatuxent Wildlife Research CenterUSA
  5. 5.EMPRES Wildlife Health and Ecology Unit, Animal Production and Health Division, Food and Agriculture Organization of the United NationsRomeItaly

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