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Visual mining of time series using a tubular visualization

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Abstract

In this paper, we study the visual mining of time series, and we contribute to the study and evaluation of 3D tubular visualizations. We describe the state of the art in the visual mining of time-dependent data, and we concentrate on visualizations that use a tubular shape to represent data. After analyzing the motivations for studying such a representation, we present an extended tubular visualization. We propose new visual encodings of the time and data, new interactions for knowledge discovery, and the use of rearrangement clustering. We show how this visualization can be used in several real-world domains and that it can address large datasets. We present a comparative user study. We conclude with the advantages and the drawbacks of our method (especially the tubular shape).

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Notes

  1. Among the Android game applications, more than 30 games can be found in which a 3D tube is used.

  2. http://emploi.nicematin.com/fckeditor/image/image-logia.

  3. For illustration, videos of DataTube2 can be found here https://www.youtube.com/watch?v=Td2cT1a4OY0 and, with a better quality, here http://www.vizassist.fr/DataTube2/.

  4. The datasets can be send to the reader upon request to the authors.

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Authors and Affiliations

  1. University of Lille2, IUT, 25-27 Rue du Maréchal Foch, 59100, Roubaix, France

    Fatma Bouali

  2. Airbus Defence and Space-Space Systems, TSEOC12 Simulation, Les Mureaux, France

    Sébastien Devaux

  3. Computer Science Laboratory, University François-Rabelais of Tours, 64 Avenue Jean Portalis, 37200, Tours, France

    Gilles Venturini

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  1. Fatma Bouali
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  2. Sébastien Devaux
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  3. Gilles Venturini
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Correspondence to Gilles Venturini.

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Bouali, F., Devaux, S. & Venturini, G. Visual mining of time series using a tubular visualization. Vis Comput 32, 15–30 (2016). https://doi.org/10.1007/s00371-014-1052-0

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