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Visualization-Based Decision Support Systems: An Example of Regional Relationship Data

Visualization-Based Decision Support Systems: An Example of Regional Relationship Data

Vicki L. Sauter, Srikanth Mudigonda, Ashok Subramanian, Ray Creely
Copyright: © 2011 |Volume: 3 |Issue: 1 |Pages: 20
ISSN: 1941-6296|EISSN: 1941-630X|EISBN13: 9781613506370|DOI: 10.4018/jdsst.2011010101
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MLA

Sauter, Vicki L., et al. "Visualization-Based Decision Support Systems: An Example of Regional Relationship Data." IJDSST vol.3, no.1 2011: pp.1-20. http://doi.org/10.4018/jdsst.2011010101

APA

Sauter, V. L., Mudigonda, S., Subramanian, A., & Creely, R. (2011). Visualization-Based Decision Support Systems: An Example of Regional Relationship Data. International Journal of Decision Support System Technology (IJDSST), 3(1), 1-20. http://doi.org/10.4018/jdsst.2011010101

Chicago

Sauter, Vicki L., et al. "Visualization-Based Decision Support Systems: An Example of Regional Relationship Data," International Journal of Decision Support System Technology (IJDSST) 3, no.1: 1-20. http://doi.org/10.4018/jdsst.2011010101

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Abstract

Increasingly, decision makers are incorporating large quantities of interrelated data in their decision making. Decision support systems need to provide visualization tools to help decision makers glean trends and patterns that will help them design and evaluate alternative actions. While visualization software that might be incorporated into decision support systems is available, the literature does not provide sufficient guidelines for selecting among possible visualizations or their attributes. This paper describes a case study of the development of a visualization component to represent regional relationship data. It addresses the specific information goals of the target organization, various constraints that needed to be satisfied, and how the goals were achieved via a suitable choice of visualization technology and visualization algorithms. The development process highlighted the need for specific visualizations to be driven by the specific problem characteristics as much as general rules of visualization. Lessons learned during the process and how these lessons may be generalized to address similar requirements is presented.

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