Seesoft-A Tool for Visualizing Line Oriented Software Statistics
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
Dynamic queries for information exploration: an implementation and evaluation
CHI '92 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Visualizing multi-dimensional data
ACM SIGGRAPH Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
A Java-Based Visual Mining Infrastructure and Applications
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
Parallel coordinates: a tool for visualizing multi-dimensional geometry
VIS '90 Proceedings of the 1st conference on Visualization '90
Interactive data visualization using focusing and linking
VIS '91 Proceedings of the 2nd conference on Visualization '91
Business process impact visualization and anomaly detection
Information Visualization
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Business process management involves many parameters and relationships and is modeled as complex business process workflows. A common way to analyze the process data is by using flowcharts. Visual analysis of a largescale chart, however, is too complex. In this case study, we employ a novel visualization technique, called VisBiz. VisBiz reduces data complexity by automatically analyzing operational data and abstracting the most critical parameters that influence business process. The basic idea is to select the most relevant parameters and layout them on a "triple-attributes" circular graph based on their relationships and user domain knowledge. VizBiz transforms the attributes to nodes and the process flows to lines. VisBiz derives a new process flow matrix to link the process of multiple circular graphs as the analyst introduces more parameters for further analysis. The results of the real-world credit card fraud study show the significant advantages of this technique in finding fraud distribution patterns and root causes of frauds.