A Visual Analysis of Multi-Attribute Data Using Pixel Matrix Displays

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HAO, Ming C., Umeshwar DAYAL, Daniel A. KEIM, Tobias SCHRECK, 2007. A Visual Analysis of Multi-Attribute Data Using Pixel Matrix Displays. Electronic Imaging 2007. San Jose, CA. In: ERBACHER, Robert F., ed., Jonathan C. ROBERTS, ed., Matti T. GRÖHN, ed., Katy BÖRNER, ed.. Visualization and Data Analysis 2007. Electronic Imaging 2007. San Jose, CA. SPIE, 649505

@inproceedings{Hao2007-01-28Visua-5468, title={A Visual Analysis of Multi-Attribute Data Using Pixel Matrix Displays}, year={2007}, doi={10.1117/12.706151}, number={6495}, publisher={SPIE}, series={SPIE Proceedings}, booktitle={Visualization and Data Analysis 2007}, editor={Erbacher, Robert F. and Roberts, Jonathan C. and Gröhn, Matti T. and Börner, Katy}, author={Hao, Ming C. and Dayal, Umeshwar and Keim, Daniel A. and Schreck, Tobias}, note={Article Number: 649505} }

Keim, Daniel A. Hao, Ming C. 2011-03-24T15:55:38Z Keim, Daniel A. A Visual Analysis of Multi-Attribute Data Using Pixel Matrix Displays Hao, Ming C. Dayal, Umeshwar Schreck, Tobias 2011-03-24T15:55:38Z 2007-01-28 eng deposit-license Paper for: IS&T/SPIE Conference on Visualization and Data Analysis (VDA 2007), January 28th - February 1st, 2007, San Jose, Ca, USA, 2007 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 information to analyze the health of their business. This paper proposes an innovative solution based on the use of pixel-matrix to represent transaction-level information within graphics. With pixel-matrixes, users can visualize areas of importance at a glance, a capability not provided by common charting techniques. Our solutions are based on colored pixel-matrixes, which are used 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. Schreck, Tobias Dayal, Umeshwar application/pdf

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