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Visual Querying of Semantically Enriched Movement Data

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Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 693))

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Abstract

Visual data exploration is used to reveal unknown patterns that, however, need to be validated, refined, and extracted for a final presentation and reporting. We contribute VESPa, a pattern-based visual query language for event sequences. With VESPa, analysts can formulate hypotheses gained and query the data for matches. In an interative analysis loop the pattern can be altered with further restrictions to narrow down the result set. Our language allows for (1) hypothesis expression and refinement, (2) visual querying, and (3) knowledge externalization. We focus on semantically enrichend movement data, used in law enforcement, consumer, and traffic analysis. To evaluate the applicability we present two case studies as well as a user study consisting of comprehensive and composition tasks.

This publication is an extended version of our IVAPP 2016 paper [1]

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Notes

  1. 1.

    For this paper, colors were chosen so as to be distinguishable both on colored and greyscale printouts. Conceptually, alternative color schemes can be chosen that are specific to user requirements, for instance, color blindness.

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Acknowledgements

This work was supported by the Horizon 2020 project CIMPLEX, grant no. 641191.

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Authors and Affiliations

  1. Institute for Visualization and Interactive Systems, University of Stuttgart, Universitätsstraße 38, 70569, Stuttgart, Germany

    Florian Haag, Robert Krüger & Thomas Ertl

Authors
  1. Florian Haag
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  2. Robert Krüger
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  3. Thomas Ertl
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Corresponding author

Correspondence to Florian Haag .

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Editors and Affiliations

  1. Escola Superior de Tecnologia do IPS, Setúbal, Portugal

    José Braz

  2. MiraLab, University of Geneva, Carouge, Switzerland

    Nadia Magnenat-Thalmann

  3. LISA - ISTIA, University of Angers, Angers, France

    Paul Richard

  4. Department of Computer Science and Electrical Engineering, Jacobs University, Bremen, Germany

    Lars Linsen

  5. University of Groningen, Groningen, The Netherlands

    Alexandru Telea

  6. Università di Catania, Catania, Italy

    Sebastiano Battiato

  7. Research Innovation Center, Canon U.S.A. Inc., San Jose, California, USA

    Francisco Imai

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Haag, F., Krüger, R., Ertl, T. (2017). Visual Querying of Semantically Enriched Movement Data. In: Braz, J., et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2016. Communications in Computer and Information Science, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-64870-5_12

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  • DOI: https://doi.org/10.1007/978-3-319-64870-5_12

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