Detecting behavior types of moving object trajectories View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-03

AUTHORS

Feda AlMuhisen, Nicolas Durand, Mohamed Quafafou

ABSTRACT

Trajectory mining is a challenging and crucial problem especially in the context of smart cities where many applications depend on human behaviors. In this paper, we characterize such behaviors by patterns, where each pattern type represents a particular behavior, e.g., emerging, latent, lost. From GPS raw data, we introduce algorithms that allow computing a formal concept lattice which encodes optimal correspondences between hidden patterns and trajectories. In order to detect behaviors, we propose an algorithm that analyzes the evolution of the discovered formal concepts over time. The method generates tagged city maps to easily visualize the resulting behaviors at different spatio-temporal granularity values. Refined or coarse analysis can thus be performed for a given situation. Experimental results using real-world GPS trajectory data show the relevance of the proposed method and the usefulness of the resulting tagged city maps. More... »

PAGES

169-187

References to SciGraph publications

  • 2015-07. Recommendations in location-based social networks: a survey in GEOINFORMATICA
  • 2016. Frequent Itemset Border Approximation by Dualization in TRANSACTIONS ON LARGE-SCALE DATA- AND KNOWLEDGE-CENTERED SYSTEMS XXVI
  • 2004. Condensed Representation of Emerging Patterns in ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING
  • 2013-12. A data mining approach for grouping and analyzing trajectories of care using claim data: the example of breast cancer in BMC MEDICAL INFORMATICS AND DECISION MAKING
  • 2011. Computing with Spatial Trajectories in NONE
  • 2015-04. Solving the data sparsity problem in destination prediction in THE VLDB JOURNAL
  • 2014. Approximation of Frequent Itemset Border by Computing Approximate Minimal Hypergraph Transversals in DATA WAREHOUSING AND KNOWLEDGE DISCOVERY
  • 2016. Semantic Pattern Mining Based Web Service Recommendation in SERVICE-ORIENTED COMPUTING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s41060-017-0076-8

    DOI

    http://dx.doi.org/10.1007/s41060-017-0076-8

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1100622247


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