A Multi Level Time Model for Interactive Multiple Dataset Visualization: The Dataset Sequencer

  • Thomas Beer
  • Gerrit Garbereder
  • Tobias Meisen
  • Rudolf Reinhard
  • Torsten Kuhlen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6939)


Integrative simulation methods are used in engineering sciences today for the modeling of complex phenomena that cannot be simulated or modeled using a single tool. For the analysis of result data appropriate multi dataset visualization tools are needed. The inherently strong relations between the single datasets that typically describe different aspects of a simulated process (e.g. phenomena taking place at different scales) demand for special interaction metaphors, allowing for an intuitive exploration of the simulated process. This work focuses on the temporal aspects of data exploration. A multi level time model and an appropriate interaction metaphor (the Dataset Sequencer) for the interactive arrangement of datasets in the time domain of the analysis space is described. It is usable for heterogeneous display systems ranging from standard desktop systems to immersive multi-display VR devices.


Dataset Sequencer Multiple Dataset Single Dataset Integrative Simulation Visualization Application 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aoyama, D.A., Hsiao, J.T.T., Cárdenas, A.F., Pon, R.K.: TimeLine and Visualization of multiple-data sets and the visualization querying challenge. J. Vis. Lang. Comput. 18, 1–21 (2007)CrossRefGoogle Scholar
  2. 2.
    Allison, J., Backman, D., Christodoulou, L.: Integrated computational materials engineering: A new paradigm for the global materials profession. JOM Journal of the Minerals, Metals and Materials Society 58, 25–27 (2006), doi:10.1007/s11837-006-0223-5CrossRefGoogle Scholar
  3. 3.
    Rajan, K.: Informatics and Integrated Computational Materials Engineering: Part II. JOM 61, 47 (2009)CrossRefGoogle Scholar
  4. 4.
    Beer, T., Meisen, T., Reinhard, R., Konovalov, S., Schilberg, D., Jeschke, S., Kuhlen, T., Bischof, C.: The Virtual Production Simulation Platform: from Collaborative Distributed Simulation to Integrated Visual Analysis. Production Engineering, 1–9 (2011), doi:10.1007/s11740-011-0326-xGoogle Scholar
  5. 5.
    Cerfontaine, P., Beer, T., Kuhlen, T., Bischof, C.H.: Towards a flexible and distributed simulation platform. In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L. (eds.) ICCSA 2008, Part I. LNCS, vol. 5072, pp. 867–882. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Schmitz, G.J., Prahl, U.: Toward a Virtual Platform for Materials Processing. JOM 61, 19–23 (2009)CrossRefGoogle Scholar
  7. 7.
    Foster, I., Kesselman, C.: The Grid 2 - Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (2004) ISBN 978-1-55860-933-4Google Scholar
  8. 8.
    Meisen, T., Meisen, P., Schilberg, D., Jeschke, S.: Application Integration of Simulation Tools Considering Domain Specific Knowledge. In: Proceedings of the International Conference on Enterprise Information Systems (2011)Google Scholar
  9. 9.
    Valette, S., Chassery, J.M.: Approximated Centroidal Voronoi Diagrams for Uniform Polygonal Mesh Coarsening. Computer Graphics Forum (Eurographics 2004 proceedings) 23(3), 381–389 (2004)CrossRefGoogle Scholar
  10. 10.
    Schroeder, W.J., Zarge, J.A., Lorensen, W.E.: Decimation of triangle meshes. SIGGRAPH Comput. Graph. 26, 65–70 (1992)CrossRefGoogle Scholar
  11. 11.
    Fowler, M.: Dealing with Role Objects. PloP (2007)Google Scholar
  12. 12.
    Stevens, W.P., Myers, G.J., Constantine, L.L.: Structured Design. IBM Systems Journal 13, 115–139 (1974)CrossRefGoogle Scholar
  13. 13.
    Khanduja, G.: Multiple Dataset Visualization (MDV) Framework for Scalar Volume Data. PhD thesis, Louisiana State University and Agricultural and Mechanical College (2009)Google Scholar
  14. 14.
    Bavoil, L., Callahan, S.P., Crossno, P.J., Freire, J., Vo, H.T.: VisTrails: Enabling interactive multiple-view visualizations. In: IEEE Visualization 2005, pp. 135–142 (2005)Google Scholar
  15. 15.
    Squillacote, A.: The Paraview Guide, 3rd edn. Kitware Inc. (2008)Google Scholar
  16. 16.
    Computational Engineering International Inc.: EnSight. Information, (last visited, 2011-05-25)
  17. 17.
    ISO/IEC 19775-1:2008: Information technology – Computer graphics and image processing – Extensible 3D (X3D) – Part 1: Architecture and base components. ISO, Geneva, Switzerland (2008)Google Scholar
  18. 18.
    Bryson, S., Johan, S.: Time management, simultaneity and time-critical computation in interactive unsteady visualization environments. In: Proceedings of the 7th Conference on Visualization, VIS 1996, pp. 255–261. IEEE Computer Society Press, Los Alamitos (1996)Google Scholar
  19. 19.
    Wolter, M., Assenmacher, I., Hentschel, B., Schirski, M., Kuhlen, T.: A Time Model for Time-Varying Visualization. Computer Graphics Forum, 1561–1571 (2009)Google Scholar
  20. 20.
    Bowman, D.A., Kruijff, E., LaViola, J.J., Poupyrev, I.: 3D User Interfaces: Theory and Practice. Addison Wesley Longman Publishing Co., Inc., Redwood City (2004)Google Scholar
  21. 21.
    Misue, K., Eades, P., Lai, W., Sugiyama, K.: Layout Adjustment and the Mental Map. Journal of Visual Languages & Computing 6, 183–210 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Thomas Beer
    • 1
  • Gerrit Garbereder
    • 1
  • Tobias Meisen
    • 2
  • Rudolf Reinhard
    • 2
  • Torsten Kuhlen
    • 1
  1. 1.Virtual Reality GroupInstitute for Scientific ComputingGermany
  2. 2.Information Management in Mechanical EngineeringRWTH Aachen UniversityGermany

Personalised recommendations