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Proceedings Paper

A visual analysis of multi-attribute data using pixel matrix displays
Author(s): Ming C. Hao; Umeshwar Dayal; Daniel Keim; Tobias Schreck
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Paper Abstract

Charts and tables are commonly used to visually analyze data. These graphics are simple and easy to understand, but charts show only highly aggregated data and present only a limited number of data values while tables often show too many data values. As a consequence, these graphics may either lose or obscure important information, so different techniques are required to monitor complex datasets. Users need more powerful visualization techniques to digest and compare detailed multi-attribute data to analyze the health of their business. This paper proposes an innovative solution based on the use of pixel-matrix displays to represent transaction-level information. With pixelmatrices, users can visualize areas of importance at a glance, a capability not provided by common charting techniques. We present our solutions to use colored pixel-matrices in (1) charts for visualizing data patterns and discovering exceptions, (2) tables for visualizing correlations and finding root-causes, and (3) time series for visualizing the evolution of long-running transactions. The solutions have been applied with success to product sales, Internet network performance analysis, and service contract applications demonstrating the benefits of our method over conventional graphics. The method is especially useful when detailed information is a key part of the analysis.

Paper Details

Date Published: 29 January 2007
PDF: 9 pages
Proc. SPIE 6495, Visualization and Data Analysis 2007, 649505 (29 January 2007); doi: 10.1117/12.706151
Show Author Affiliations
Ming C. Hao, Hewlett-Packard Labs. (United States)
Umeshwar Dayal, Hewlett-Packard Labs. (United States)
Daniel Keim, Univ. Konstanz (Germany)
Tobias Schreck, Univ. Konstanz (Germany)

Published in SPIE Proceedings Vol. 6495:
Visualization and Data Analysis 2007
Robert F. Erbacher; Jonathan C. Roberts; Matti T. Gröhn; Katy Börner, Editor(s)

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