Advertisement

The Visual Computer

, Volume 32, Issue 1, pp 15–30 | Cite as

Visual mining of time series using a tubular visualization

  • Fatma Bouali
  • Sébastien Devaux
  • Gilles VenturiniEmail author
Original Article

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).

Keywords

Visual data mining Time series  3D interactive visualizations User evaluation 

References

  1. 1.
    Aigner, W., Miksch, S., Muller, W., Schumann, H., Tominski, C.: Visualizing time-oriented data-a systematic view. Comput. Graph. 31(3), 401–409 (2007)CrossRefGoogle Scholar
  2. 2.
    Aigner, W., Miksch, S., Schumann, H.: Visualization of Time-Oriented Data. Human-Computer Interaction Series. Springer, Berlin (2011)CrossRefGoogle Scholar
  3. 3.
    Aigner, W., Rind, A., Hoffmann, S.: Comparative evaluation of an interactive time-series visualization that combines quantitative data with qualitative abstractions. In: Computer Graphics Forum, vol. 31, pp. 995–1004. Wiley Online Library (2012)Google Scholar
  4. 4.
    Andrienko, G., Andrienko, N.: Dynamic time transformations for visualizing multiple trajectories in interactive space-time cube. In: International Cartographic Conference, ICC (2011)Google Scholar
  5. 5.
    Ankerst, M.: Visual data mining with pixel-oriented visualization techniques. In: Proceedings of the ACM SIGKDD Workshop on Visual Data Mining (2001)Google Scholar
  6. 6.
    Ankerst, M., Berchtold, S., Keim, D.A.: Similarity clustering of dimensions for an enhanced visualization of multidimensional data. In: Proceedings of the 1998 IEEE symposium on information visualization. INFOVIS ’98, pp. 52–60. IEEE Computer Society, Washington, DC, USA (1998)Google Scholar
  7. 7.
    Ankerst, M., Keim, D.A., Kriegel, H.P.: Circle segments: a technique for visually exploring large multidimensional data sets. In: Proceedings of IEEE Visualization’96, Hot Topics 96 (1996)Google Scholar
  8. 8.
    Antunes, C.M., Oliveira, A.L.: Temporal data mining: An overview. KDD Workshop on Temporal Data Mining (2001)Google Scholar
  9. 9.
    Aris, A., Shneiderman, B., Plaisant, C., Shmueli, G., Jank, W.: Representing unevenly-spaced time series data for visualization and interactive exploration. In: Human-Computer Interaction-INTERACT 2005, pp. 835–846. Springer (2005)Google Scholar
  10. 10.
    Beardsley, T.: PROFILE: humans unite! scientific american (1999). http://www.cs.ucsd.edu/users/goguen/courses/171sp02/shneiderman.html
  11. 11.
    Bertin, J.: La graphique et le traitement graphique de l’information. Nouvelle Bibliothèque Scientifique. (1977)Google Scholar
  12. 12.
    Carlis, J.V., Konstan, J.A.: Interactive visualization of serial periodic data. In: Proceedings of the 11th annual ACM symposium on User interface software and technology pp. 29–38 (1998)Google Scholar
  13. 13.
    Cleveland, W.S.: Visualizing Data. Hobart Press, Summit (1993)Google Scholar
  14. 14.
    Craig, P., Roa-Seiler, N.: A vertical timeline visualization for the exploratory analysis of dialogue data. In: Information Visualisation (IV), 2012 16th International Conference on IEEE, pp. 68–73. (2012)Google Scholar
  15. 15.
    Daassi, C., Dumas, M., Fauvet, M.C., Nigay, L., Scholl, P.C.: Visual exploration of temporal object databases. In: proceedinga of BDA00 conference pp. 24–27 (2000)Google Scholar
  16. 16.
    Francis, B., Pritchard, J.: Visualisation of historical events using Lexis pencils. Case Stud. Vis. Soc. Sci. 30 (2003)Google Scholar
  17. 17.
    Hackstadt, S.T., Malony, A.D.: Visualizing parallel program and performance data with IBM Visualisation data explorer. Master’s thesis (1994)Google Scholar
  18. 18.
    Hao, M.C., Marwah, M., Janetzko, H., Dayal, U., Keim, D.A., Patnaik, D., Ramakrishnan, N., Sharma, R.K.: Visual exploration of frequent patterns in multivariate time series. Inf. Vis. 11(1), 71–83 (2012)CrossRefGoogle Scholar
  19. 19.
    Hébrail, G., Debregeas, A.: Interactive interpretation of Kohonen maps applied to curves. In: Proceedings of the 4th international conference on knowledge discovery and data mining. AAAI press, Menlo Park pp. 179–183 (1998)Google Scholar
  20. 20.
    Heer, J., Shneiderman, B.: Interactive dynamics for visual analysis. Queue 30(30), 30–55 (2012)CrossRefGoogle Scholar
  21. 21.
    Hofmann, H., Follett, L., Majumder, M., Cook, D.: Graphical tests for power comparison of competing designs. IEEE Trans. Vis. Comput. Graph 18(12), 2441–2448 (2012)CrossRefGoogle Scholar
  22. 22.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. (CSUR) 31(3), 264–323 (1999)CrossRefGoogle Scholar
  23. 23.
    Kandogan, E.: Star coordinates: a multi-dimensional visualization technique with uniform treatment of dimensions. IEEE Symp. Inf. Vis. 2000, 4–8 (2000)Google Scholar
  24. 24.
    Keim, D.A., Ankerst, M., Kriegel, H.P.: Recursive pattern: a technique for visualizing very large amounts of data. In: Proceedings of the 6th conference on visualization’95 pp. 279–286 (1995)Google Scholar
  25. 25.
    Keim, D.A., Kohlhammer, J., Ellis, G., Mansmann, F. (eds.): Mastering the information age—solving problems with visual analytics. Eurographics (2010). http://www.vismaster.eu/book/
  26. 26.
    Keim, D.A., Kriegel, H.P.: Visualization techniques for mining large databases: a comparison. IEEE Trans. Knowl. Data Eng. 8(6), 923–938 (1996)CrossRefGoogle Scholar
  27. 27.
    Kincaid, R., Lam, H.: Line graph explorer: scalable display of line graphs using focus+context. In: Proceedings of the working conference on advanced visual interfaces, AVI ’06, pp. 404–411. ACM (2006)Google Scholar
  28. 28.
    Krstajic, M., Bertini, E., Keim, D.: Cloudlines: compact display of event episodes in multiple time-series. IEEE Trans. Vis. Comput. Graph. 17(12), 2432–2439 (2011)CrossRefGoogle Scholar
  29. 29.
    Lin, J., Keogh, E., Lonardi, S., Lankford, J.P., Nystrom, D.M.: Viztree: a tool for visually mining and monitoring massive time series databases. In: Proceedings of international conference on very large data bases, pp. 1269–1272 (2004)Google Scholar
  30. 30.
    Mackinlay, J.D., Robertson, G.G., Card, S.K.: The perspective wall: Detail and context smoothly integrated. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp. 173–176. ACM (1991)Google Scholar
  31. 31.
    McCormick, W.T., Schweitzer, P.J., White, T.W.: Problem decomposition and data reorganization by a clustering technique. Operat. Res. 20 5(5), 993–1009 (1972)CrossRefGoogle Scholar
  32. 32.
    McLachlan, P., Munzner, T., Koutsofios, E., North, S.: Liverac: interactive visual exploration of system management time-series data. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp. 1483–1492. ACM (2008)Google Scholar
  33. 33.
    Minard, C.J.: Carte figurative des pertes successives en hommes de l’Armée Française dans la campagne de Russie. pp. 1812–1813 (1861)Google Scholar
  34. 34.
    Mitchell, K., Kennedy, J.: The perspective tunnel: an inside view on smoothly integrating detail and context. In: Visualization in scientific computing ’97: proceedings of the Eurographics Workshop, Springer Computing Science. Springer (1997)Google Scholar
  35. 35.
    Muller, W., Schumann, H.: Visualization methods for time-dependent data-an overview. Simulation conference, 2003. In Proceedings of the 2003 Winter 1, pp. 737–745 (2003)Google Scholar
  36. 36.
    Nekrasovski, D., Bodnar, A., McGrenere, J., Guimbretière, F., Munzner, T.: An evaluation of pan and zoom and rubber sheet navigation with and without an overview. In: Proceedings of the SIGCHI conference on Human Factors in computing systems, pp. 11–20. ACM (2006)Google Scholar
  37. 37.
    Oelke, D., Janetzko, H., Simon, S., Neuhaus, K., Keim, D.A.: Visual boosting in pixel-based visualizations. In: Computer Graphics Forum, vol. 30, pp. 871–880. Wiley Online Library (2011)Google Scholar
  38. 38.
    Shmueli, G., Jank, W., Aris, A., Plaisant, C., Shneiderman, B.: Exploring auction databases through interactive visualization. Decis. Support Syst. 42(3), 1521–1538 (2006)CrossRefGoogle Scholar
  39. 39.
    Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: IEEE visual languages, UMCP-CSD CS-TR-3665, pp. 336–343. College Park, Maryland 20742, U.S.A. (1996). http://www.citeseer.ist.psu.edu/shneiderman96eyes.html
  40. 40.
    Suntinger, M., Obweger, H., Schuh, J., Gröller, M.E.: Event tunnel: exploring event-driven business processes. IEEE Comput. Graph. Appl. 28(5), 46–55 (2008)CrossRefGoogle Scholar
  41. 41.
    Sureau, F., Plantard, F., Bouali, F., Venturini, G.: Visual mining of web logs with DataTube2. In: Tenth international conference on web information system engineering (WISE), LNCS, Springer, pp. 555–562 (2008)Google Scholar
  42. 42.
    Theron, R.: Hierarchical-temporal data visualization using a tree-ring metaphor. Lect. Notes Comput. Sci. 4073(2006), 70–81 (2006)CrossRefGoogle Scholar
  43. 43.
    Tominski, C., Schumann, H.: Enhanced interactive spiral display. In: Proceedings of the annual SIGRAD conference, special theme: interactivity, pp. 53–56 (2008)Google Scholar
  44. 44.
    Wattenberg, M.: Arc diagrams: visualizing structure in strings. information visualization, 2002. INFOVIS 2002. IEEE symposium on pp. 110–116 (2002)Google Scholar
  45. 45.
    Weber, M., Alexa, M., Muller, W.: Visualizing time-series on spirals. Information visualization, 2001. INFOVIS 2001. IEEE symposium on pp. 7–13 (2001)Google Scholar
  46. 46.
    van Wijk, J.J., van Selow, E.R.: Cluster and calendar based visualization of time series data. In: Proceedings of IEEE symposium on information visualization pp. 4–9 (1999)Google Scholar
  47. 47.
    Wong, P.C., Bergeron, R.D.: 30 years of multidimensional multivariate visualization. Scientific visualization—Overviews. Methodologies and Techniques, pp. 3–33. IEEE Computer Society Press, Los Alamitos, CA (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Fatma Bouali
    • 1
  • Sébastien Devaux
    • 2
  • Gilles Venturini
    • 3
    Email author
  1. 1.University of Lille2, IUTRoubaixFrance
  2. 2.Airbus Defence and Space-Space Systems, TSEOC12 SimulationLes MureauxFrance
  3. 3.Computer Science LaboratoryUniversity François-Rabelais of ToursToursFrance

Personalised recommendations