Enhancing the visual clustering of query-dependent database visualization techniques using screen-filling curves

  • Daniel A. Keim
Workshop Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1183)


An important goal of visualization technology is to support the exploration and analysis of very large databases. Visualization techniques may help in database exploration by providing a comprehensive overview of the database. Pixel-oriented visualization techniques have been developed to visualize as many data items as possible on the display at one point of time. The basic idea of pixeloriented techniques is to map each data value to a colored pixel and present the data values belonging to different dimensions (attributes) in separate subwindows. In case of the query-dependent techniques, the pixels are arranged and colored according to the relevance for the query, providing a visual impression of the query result and of its relevance with respect to the query. One problem of the current query-dependent pixel-oriented visualization techniques is that their local clustering properties are insufficient. In this paper, we therefore generalize the original pixel-oriented techniques and propose new variants which retain the overall arrangement but enhance the clustering properties by using screen-filling curves locally. Different screen-filling curves (Snake, Peano-Hilbert, Morton) with different sizes (2, 4, 8, 16) may be used. We evaluate the possible variants and compare the resulting visualizations. The visualizations show that screen-filling curves clearly enhance the visual clustering of query-dependent pixel-oriented visualization techniques, but it also becomes clear that there is no significant difference between the different screen-filling curves.


Visualizing Large Data Sets Visualizing Multidimensional Multivariate Data Database Exploration Visual Query Systems 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [ADLP 95]
    Anupam V., Dar S., Leibfried T., Petajan E.: ‘DataSpace: 3-D Visualization of Large Databases', Proc. Int. Symp. on Information Visualization, Atlanta, GA, 1995, pp. 82–88.Google Scholar
  2. [And 72]
    Andrews D. F.: ‘Plots of High-Dimensional Data', Biometrics, Vol. 29, 1972, pp. 125–136.Google Scholar
  3. [AS 94]
    Ahlberg C., Shneiderman B.: ‘Visual Information Seeking: Tight Coupling of Dynamic Query Filters with Starfield Displays', Proc. ACM CHI Int. Conf. on Human Factors in Computing (CHI'94), Boston, MA, 1994, pp. 313–317.Google Scholar
  4. [Asi 85]
    Asimov D.: ‘The Grand Tour: A Tool For Viewing Multidimensional Data', SIAM Journal of Science & Stat. Comp., Vol. 6, 1985, pp. 128–143.Google Scholar
  5. [AWS 92]
    Ahlberg C., Williamson C., Shneiderman B.: ‘Dynamic Queries for Information Exploration: An Implementation and Evaluation', Proc. ACM CHI Int. Conf. on Human Factors in Computing (CHI'92), Monterey, CA, 1992, pp. 619–626.Google Scholar
  6. [BF 90]
    Beshers C., Feiner S.: ‘Visualizing n-Dimensional Virtual Worlds with n-Vision', Computer Graphics, Vol. 24, No. 2, 1990, pp. 37–38.Google Scholar
  7. [BMMS 91]
    Buja A., McDonald J. A., Michalak J., Stuetzle W.: ‘Interactive Data Visualization Using Focusing and Linking', Visualization'91, San Diego, CA, 1991, pp. 156–163.Google Scholar
  8. [Che 73]
    Chernoff H.: ‘The Use of Faces to Represent Points in k-Dimensional Space Graphically', Journal Amer. Statistical Association, Vol. 68, pp 361–368.Google Scholar
  9. [Cle 93]
    Cleveland W. S.: ‘Visualizing Data', AT&T Bell Laboratories, Murray Hill, NJ, Hobart Press, Summit NJ, 1993.Google Scholar
  10. [Eic 94]
    Eick S.: ‘Data Visualization Sliders', Proc. ACM UIST'94, 1994, pp. 119–120.Google Scholar
  11. [Hil 91]
    Hilbert D.: ‘Über stetige Abbildung einer Line auf ein Flächenstück', Math. Annalen, Vol. 38, 1891, pp. 459–460.Google Scholar
  12. [Hub 85]
    Huber P. J.: ‘Projection Pursuit', The Annals of Statistics, Vol. 13, No. 2, 1985, pp. 435–474.Google Scholar
  13. [ID 90]
    Inselberg A., Dimsdale B.: ‘Parallel Coordinates: A Tool for Visualizing Multi-Dimensional Geometry', Visualization '90, San Francisco, CA, 1990, pp. 361–370.Google Scholar
  14. [Kei 94]
    Keim D. A.: ‘Visual Support for Query Specification and Data Mining', Ph.D. Dissertation, University of Munich, July 1994, Shaker-Publishing Company, Aachen, Germany, 1995, ISBN 3-8265-0594-8.Google Scholar
  15. [KK 94]
    Keim D. A., Kriegel H.-P.: ‘VisDB: Database Exploration using Multidimensional Visualization', Computer Graphics & Applications, Sept. 1994, pp. 40–49.Google Scholar
  16. [KKA 95]
    Keim D. A., Kriegel H.-P., Ankerst M.: ‘Recursive Pattern: A Technique for Visualizing Very Large Amounts of Data', Proc. Visualization '95, Atlanta, GA, 1995, pp. 279–286.Google Scholar
  17. [KKS 94]
    Keim D. A., Kriegel H.-P., Seidl T.: ‘Supporting Data Mining of Large Databases by Visual Feedback Queries', Proc. 10th Int. Conf. on Data Engineering, Houston, TX, 1994, pp. 302–313.Google Scholar
  18. [Mor 66]
    Morton G. M.: ‘A Computer Oriented Geodetic Data Base and a New Technique in File Sequencing', IBM Ltd. Ottawa, Canada, 1966.Google Scholar
  19. [Pea 90]
    Peano G.: ‘Sur une courbe qui remplit toute une aire plaine', Math. Annalen, Vol. 36, 1890, pp. 157–160.Google Scholar
  20. [SGB 91]
    Smith S., Grinstein G., Bergeron R. D.: ‘Interactive Data Exploration with a Supercomputer', Visualization '91, San Diego, CA, 1991, pp. 248–254.Google Scholar
  21. [Shn 92]
    Shneiderman B.: ‘Tree Visualization with Treemaps: A 2-D Space-filling Approach', ACM Trans. on Graphics, Vol. 11, No. 1, 1992, pp. 92–99.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Daniel A. Keim
    • 1
  1. 1.Institute for Computer ScienceUniversity of MunichMunichGermany

Personalised recommendations