Smart Lenses

  • Conrad Thiede
  • Georg Fuchs
  • Heidrun Schumann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5166)


Focus + context techniques are widely used for the efficient visualization of large data sets. However, the corresponding adaptation of the representation to the task at hand is not trivial, requiring a suitable model of the visualization goal. One type of focus + context technique is the use of lenses, interactive tools that modify the visualization in a locally confined region and can be ’stacked’ to create complex filters. In this paper we propose a new approach to intergrate smart lenses into the visualization process based on Chi’s Data State Reference Model.This allows us to automatically adapt specific aspects of the visualization based on relevant influence factors, without complex task modeling.


Data Element Transformation Operator Open Geospatial Consortium Visualization Process Directional Texture 
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.


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  1. 1.
    Ellis, G., Dix, A.: Enabling Automatic Clutter Reduction in Parallel Coordinate Plots. IEEE Transactions on Visualization and Computer Graphics 12(5), 717–724 (2006)CrossRefGoogle Scholar
  2. 2.
    Andrienko, N.V., Andrienko, G.L.: Exploratory Analysis of Spatial and Temporal Data – A Systematic Approach. Springer, Heidelberg (2006)MATHGoogle Scholar
  3. 3.
    Bier, E., Stone, M., Pier, K., Buxton, W., DeRose, T.: Toolglass and Magic Lenses: the See-Through Interface. In: Proceedings of the 20th annual conference on Computer graphics and interactive techniques, pp. 73–80 (1993)Google Scholar
  4. 4.
    Fuchs, G., Thiede, C., Schumann, H.: Pluggable Lenses for Interactive Visualizations. In: Poster Compendium of InfoVis 2007 (October 2007)Google Scholar
  5. 5.
    Chi, E.: A Taxonomy of Visualization Techniques Using the Data State Reference Model. In: IEEE InfoVis., pp. 69–75 (2000)Google Scholar
  6. 6.
    Griethe, H., Fuchs, G., Schumann, H.: A Classification Scheme for Lens Technique. In: WSCG Short Papers, pp. 89–92 (2005)Google Scholar
  7. 7.
    Ellis, G., Dix, A.: A Taxonomy of Clutter Reduction for Information Visualisation. IEEE Transactions on Visualization and Computer Graphics 13(6), 1216–1223 (2007)CrossRefGoogle Scholar
  8. 8.
    Keahey, T.A.: The Generalized Detail-In-Context Problem. In: IEEE InfoVis., Washington, DC, USA, pp. 44–51. IEEE Computer Society, Los Alamitos (1998)Google Scholar
  9. 9.
    Fuchs, G.A., Holst, M., Schumann, H.: 3D Mesh Exploration for Smart Visual Interfaces. In: Proc. Intl. Conference on Visual Information Systems (2008)Google Scholar
  10. 10.
    IDELIX Software Inc.: Pliable Display Technology White Paper,
  11. 11.
    Keahey, T.: Area-normalized thematic views. In: Proceedings of International Cartography Assembly (1999)Google Scholar
  12. 12.
    Loughlin, M., Hughes, J.: An Annotation System for 3D Fluid Flow Visualization. In: Proceedings of IEEE Conference on Visualization 1994, October 17-21, 1994, pp. 273–279, CP31 (1994)Google Scholar
  13. 13.
    Tominski, C., Abello, J., van Ham, F., Schumann, H.: Fisheye Tree Views and Lenses for Graph Visualization. IV, pp. 17–24 (2006)Google Scholar
  14. 14.
    Bier, E., Stone, M., Pier, K.: Enhanced Illustration Using Magic Lens Filters. Computer Graphics and Applications, IEEE 17(6), 62–70 (1997)CrossRefGoogle Scholar
  15. 15.
    Rase, W.D.: Fischauge-Projektionen als kartographische Lupen. In: Salzburger Kartographische Materialien, vol. 26, pp. 115–122 (1997)Google Scholar
  16. 16.
    Leung, Y.K., Apperley, M.D.: A Review and Taxonomy of Distortion-Oriented Presentation Techniques. ACM Trans. Comput.-Hum. Interact. 1(2) (1994)Google Scholar
  17. 17.
    Kosara, R., Miksch, S., Hauser, H.: Semantic Depth of Field. In: IEEE InfoVis. (2001)Google Scholar
  18. 18.
    Tominski, C., Fuchs, G., Schumann, H.: Task-Driven Color Coding. In: Proc. 12th International Conference Information Visualisation (IV 2008), London, July 8 - 11, 2008. IEEE Computer Society, Los Alamitos (2008)Google Scholar
  19. 19.
    Biehl, N., Düsterhöft, A., Forbrig, P., Fuchs, G., Reichart, D., Schumann, H.: Advanced Multi-Modal User Interfaces for Mobile Devices - Integration of Visualization, Speech Interaction and Task Modeling. In: Proceedings 17th IRMA International Conference, Washington DC, USA (2006) (Short Paper)Google Scholar
  20. 20.
    Rathsack, R., Wolff, A., Forbrig, P.: Using HCI-Patterns with Model-Based Generation of Advanced User Interfaces. In: MDDAUI 2006 - Model Driven Development of Advanced User Interfaces, vol. 214 (2006)Google Scholar
  21. 21.
    Open Geospatial Consortium: OpenGIS Filter Encoding Implementation Specification, Version 1.1.0 (final). Open Geospatial Consortium Inc. (2005)Google Scholar
  22. 22.
    Schulze-Wollgast, P., Schumann, H., Tominski, C.: Visual Analysis of Human Health Data. In: IRMA International Conference (2003)Google Scholar
  23. 23.
    Havre, S., Hetzler, B., Nowell, L.: ThemeRiver: Visualizing theme Changes Over Time. In: IEEE InfoVis., pp. 115–123. IEEE Computer Society, Los Alamitos (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Conrad Thiede
    • 1
  • Georg Fuchs
    • 1
  • Heidrun Schumann
    • 1
  1. 1.Institute for Computer ScienceUniversity of Rostock 

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