A visual analysis of multi-attribute data using pixel matrix displays

Ming C. Hao; Umeshwar Dayal; Daniel Keim; Tobias Schreck

doi:10.1117/12.706151

29 January 2007 A visual analysis of multi-attribute data using pixel matrix displays

Ming C. Hao, Umeshwar Dayal, Daniel Keim, Tobias Schreck

Proceedings Volume 6495, Visualization and Data Analysis 2007; 649505 (2007) https://doi.org/10.1117/12.706151
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

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.

Citation Download Citation

Ming C. Hao, Umeshwar Dayal, Daniel Keim, and Tobias Schreck "A visual analysis of multi-attribute data using pixel matrix displays", Proc. SPIE 6495, Visualization and Data Analysis 2007, 649505 (29 January 2007); https://doi.org/10.1117/12.706151

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