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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)

Abstract

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.

Keywords

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

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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

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

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