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The Perceptual Eye View: A User-Defined Method for Information Visualization

  • Liang-Hong Wu
  • Ping-Yu Hsu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4551)

Abstract

With the growing volumes of data, exploring the relationships within the huge amounts of data is difficult. Information visualization uses the human perception system to assist users in analyzing complex relationships and graphical hierarchy trees used commonly to present the relationship among the data. Conventional information visualization approaches fail to consider human factors, they only provide fixed degree of detail to different users. However, different users have different perceptions. A well-known information visualization called ’Magic Eye View’ uses a three-dimensional interaction to allow the user to control the degree of detail he would like. However, it fails to consider some important focus + context features such as the smooth transition of the focus region and the global context. In this paper, we propose a novel information visualization method, called the ’Perceptual Eye View,’ by which users may control the focus points three-dimensionally enabling different users to view their user-defined degree of detail of information space and to perceive based on their own knowledge and perception. The results demonstrate that our proposed method improve the ’Magic Eye View’ by providing smooth transition of the focus region and the global context, which are important focus+context features that the ’Magic Eye View’ fails to consider.

Keywords

Human-Computer Interaction Information Visualization Human Perception 

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References

  1. 1.
    Beard, D.V., Walker, J.Q.: Navigational Techniques to Improve the Display of Large 2-D Spaces. Behaviour and Information Technology 9(6), 451–466 (1990)CrossRefGoogle Scholar
  2. 2.
    Card, S., Mackinlay, J., Shneiderman, B.: Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann Publishers Inc., San Francisco (1999)Google Scholar
  3. 3.
    Carpendale, M.S.T., Cowperthwaite, D.J., Fracchia, F.D.: Three-Dimensional Pliable Surfaces: For Effective Presentation of Visual Information. In: Proceedings of the 8th annual ACM Symposium on User Interface and Software Technology, pp. 217–226. ACM Press, New York (1995)CrossRefGoogle Scholar
  4. 4.
    Foley, J.: Getting There: The Ten Top Problems Left. IEEE Computer Graphics and Applications 20(1), 66–68 (2000)CrossRefGoogle Scholar
  5. 5.
    Furnas, G.W.: Generalized Fisheye Views. In: Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, pp. 16–23. ACM, New York (1986)Google Scholar
  6. 6.
    Gershon, N., Eick, S.G.: Information visualization. IEEE Computer Graphics and Applications 17(4), 29–31 (1997)CrossRefGoogle Scholar
  7. 7.
    Han, J., Hu, X., Cercone, N.: A visualization model of interactive knowledge discovery systems and its implementations. Information Visualization 2, 105–125 (2003)CrossRefGoogle Scholar
  8. 8.
    Keim, D.A.: Information Visualizattion and Visual Data Mining. IEEE Transactions on Visualization and Computer Graphics 8(1), 1–8 (2002)CrossRefGoogle Scholar
  9. 9.
    Kreuseler, M., Schumann, H.: A Flexible Approach for Visual Data Mining. IEEE Transactions on Visualization and Computer Graphics 8(1), 39–51 (2002)CrossRefGoogle Scholar
  10. 10.
    Lamping, J., Rao, R.: The Hyperbolic Browser: A Focus+Context Technique for Visualizing Large Hierarchies. Journal of Visual Languages and Computing 7(1), 33–55 (1996)CrossRefGoogle Scholar
  11. 11.
    Lamping, J., Rao, R., Pirolli, P.: A Focus + Context Technique Based on Hyperbolic Geometry for Visualizing Large Hierarchies. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Denver, Colorado, United States, pp. 401–408 (1995)Google Scholar
  12. 12.
    McCormick, B.H., DeFanti, T.A., Brown, M.D.: Visualization in Scientific Computing. Computer Graphics 21(6), 1345–1366 (1987)Google Scholar
  13. 13.
    Munzner, T.: Exploring Large Graphs in 3D Hyperbolic Space. IEEE Computer Graphics and Applications 18(4), 18–23 (1998)CrossRefGoogle Scholar
  14. 14.
    Sarkar, M., Brown, M.H.: Graphical Fisheye Views. Communications of the ACM 37(12), 73–84 (1994)CrossRefGoogle Scholar
  15. 15.
    Sarkar, M., Snibbe, S.S., Tversky, O.J., Reiss, S.P.: Stretching the Rubber Sheet: A Metaphor for Viewing Large Layouts on Small Screens. In: Proceedings of the 6th Annual ACM Symposium on User Interface Software and Technology, pp. 81–91(1993)Google Scholar
  16. 16.
    Schaffer, D., Zuo, Z., Greenberg, S., Bartram, L., Dill, J., Dubs, S., Roseman, M.: Navigating Hierarchically Clustered Networks through Fisheye and Full-Zoom Methods. ACM Transactions on Computer Human Interaction 3(2), 162–188 (1996)CrossRefGoogle Scholar
  17. 17.
    Tory, M., Moller, T.: Human Factors In Visualization Research. IEEE Transactions on Visualization and Computer Graphics 10(1), 1–13 (2004)CrossRefGoogle Scholar
  18. 18.
    Ward, M.O.: A taxonomy of glyph placement strategies for multidimensional data visualization. Information Visualization 1, 194–210 (2002)CrossRefGoogle Scholar
  19. 19.
    Ware, C., Franck, G.: Evaluating Stereo and Motion Cues for Visualizing Information Nets in Three Dimensions. ACM Transactions on Graphics 15, 121–139 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Liang-Hong Wu
    • 1
  • Ping-Yu Hsu
    • 1
  1. 1.Department of Business Administration, National Central University, Chung-Li, Taiwan 320R.O.C.

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