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Volume 33 Issue 4
July  2018
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Guochen Cai, Kyungmi Lee, Ickjai Lee. Mining Semantic Trajectory Patterns from Geo-Tagged Data[J]. Journal of Computer Science and Technology, 2018, 33(4): 849-862. DOI: 10.1007/s11390-018-1860-1
Citation: Guochen Cai, Kyungmi Lee, Ickjai Lee. Mining Semantic Trajectory Patterns from Geo-Tagged Data[J]. Journal of Computer Science and Technology, 2018, 33(4): 849-862. DOI: 10.1007/s11390-018-1860-1
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Mining Semantic Trajectory Patterns from Geo-Tagged Data

  • Information Technology Academy, College of Business, Law and Governance, James Cook University Queensland, QLD 4870, Australia

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  • Author Bio:

    Guochen Cai received his Ph.D. degree, in 2017, in information technology from James Cook University, Queensland. His current research areas include trajectory data mining, semantic trajectories, recommender systems, and spatio-temporal data mining.

  • Corresponding author:

    Ickjai Lee,E-mail:Ickjai.Lee@jcu.edu.au

  • Received Date: March 30, 2017
  • Revised Date: May 16, 2018
  • Published Date: July 04, 2018
    Abstract
  • User-generated social media data tagged with geographic information present messages of dynamic spatiotemporal trajectories. These increasing mobility data provide potential opportunities to enhance the understanding of human mobility behaviors. Several trajectory data mining approaches have been proposed to benefit from these rich datasets, but fail to incorporate aspatial semantics in mining. This study investigates mining frequent moving sequences of geographic entities with transit time from geo-tagged data. Different from previous analysis of geographic feature only trajectories, this work focuses on extracting patterns with rich context semantics. We extend raw geographic trajectories generated from geo-tagged data with rich context semantic annotations, use regions-of-interest as stops to represent interesting places, enrich them with multiple aspatial semantic annotations, and propose a semantic trajectory pattern mining algorithm that returns basic and multidimensional semantic trajectory patterns. Experimental results demonstrate that semantic trajectory patterns from our method present semantically meaningful patterns and display richer semantic knowledge.
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    • References (19)
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