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Visual Trajectory Pattern Mining: An Exploratory Study in Baggage Handling Systems

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Advances in Data Mining. Applications and Theoretical Aspects (ICDM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8557))

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Abstract

There is currently a huge amount of data being collected about movements of objects. Such data is called spatiotemporal data and paths left by moving-objects are called trajectories. Recently, researchers have been targeting those trajectories for extracting interesting and useful knowledge by means of pattern analysis and data mining. But, it is difficult to analyse huge datasets of trajectories without summarizing them and visualizing them for the knowledge seeker and for the decision makers. Therefore, this research paper focuses on utilizing visual techniques and data mining analysis of trajectory patterns in order to help extract patterns and knowledge in an interactive approach. The research study proposes a research framework which integrates multiple data analysis and visualization techniques in a coherent architecture in support of interactive trajectory pattern visualization for the decision makers. An application case-study of the techniques is conducted on an airport’s baggage movement data within the Baggage Handling System (BHS). The results indicate the feasibility of the approach and its methods in visually analysing trajectory patterns in an interactive approach which can support the decision maker.

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Author information

Authors and Affiliations

  1. Teradata Corporation, USA

    Ayman Al-Serafi

  2. Department of Business Informatics & Operations, German University in Cairo (GUC), Cairo, Egypt

    Ahmed Elragal

Authors
  1. Ayman Al-Serafi
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  2. Ahmed Elragal
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Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, IBaI, Leipzig, Germany

    Petra Perner

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Al-Serafi, A., Elragal, A. (2014). Visual Trajectory Pattern Mining: An Exploratory Study in Baggage Handling Systems. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2014. Lecture Notes in Computer Science(), vol 8557. Springer, Cham. https://doi.org/10.1007/978-3-319-08976-8_12

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  • DOI: https://doi.org/10.1007/978-3-319-08976-8_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08975-1

  • Online ISBN: 978-3-319-08976-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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