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Towards Interactive Visualization of Time Series Data to Support Knowledge Discovery

  • Conference paper
  • First Online:
Progress in Artificial Intelligence (EPIA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9273))

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Abstract

Higher education institutions have a significant interest in increasing the educational quality and effectiveness. A major challenge in modern education is the large amount of time-dependent data, which requires efficient tools and methods to improve decision making. Methods like motion charts (MC) show changes over time by presenting animations in two-dimensional space and by changing element appearances. In this paper, we present a visual analytics tool which makes use of enhanced animated data visualization methods. The tool is primarily designed for exploratory analysis of academic analytics (AA) and offers several interactive visualization methods that enhance the MC design. An experiment is conducted to evaluate the efficacy of both static and animated data visualization methods. To interpret the experiment results, we utilized one-way repeated measures ANOVA.

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Author information

Authors and Affiliations

  1. KD Lab, Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Jan Géryk

Authors
  1. Jan Géryk
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Corresponding author

Correspondence to Jan Géryk .

Editor information

Editors and Affiliations

  1. ISEC - Coimbra Institute of Engineering, Polytechnic Institute of Coimbra, Coimbra, Portugal

    Francisco Pereira

  2. CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Penousal Machado

  3. CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Ernesto Costa

  4. CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Amílcar Cardoso

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© 2015 Springer International Publishing Switzerland

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Géryk, J. (2015). Towards Interactive Visualization of Time Series Data to Support Knowledge Discovery. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 2015. Lecture Notes in Computer Science(), vol 9273. Springer, Cham. https://doi.org/10.1007/978-3-319-23485-4_57

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  • DOI: https://doi.org/10.1007/978-3-319-23485-4_57

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23484-7

  • Online ISBN: 978-3-319-23485-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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