Visual exploration of large data sets
Communications of the ACM
The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations
VL '96 Proceedings of the 1996 IEEE Symposium on Visual Languages
Parallel coordinates: a tool for visualizing multi-dimensional geometry
VIS '90 Proceedings of the 1st conference on Visualization '90
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
IEEE Computer Graphics and Applications
Parallel Sets: Interactive Exploration and Visual Analysis of Categorical Data
IEEE Transactions on Visualization and Computer Graphics
Challenges in Visual Data Analysis
IV '06 Proceedings of the conference on Information Visualization
Information Visualization: Design for Interaction (2nd Edition)
Information Visualization: Design for Interaction (2nd Edition)
Intelligent Data Analysis - Philosophies and Methodologies for Knowledge Discovery
IEEE Computer Graphics and Applications
Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines
IEEE Transactions on Visualization and Computer Graphics
Short communication: Ten guidelines for effective data visualization in scientific publications
Environmental Modelling & Software
IEEE Transactions on Visualization and Computer Graphics
Development of a clinical data warehouse from an intensive care clinical information system
Computer Methods and Programs in Biomedicine
Interactive dynamics for visual analysis
Communications of the ACM
Computer Methods and Programs in Biomedicine
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Epidemiology requires the analysis and visualization of massive data sets. The field of cancer statistics in particular is facing the challenging task of visualizing a large data set that contains a wide range of available dimensions. The existing work of epidemiologists has been time-consuming because of visualization techniques that could not be scaled to support an unguided exploration process. This limitation has led to the inefficient use of data representations that are mainly used for detailed analysis. Our goal was to find a scalable visualization technique that focused on covering a wide range of categorical information. For this purpose, a task by data type taxonomy is used to analyze the existing data visualization techniques. The chosen representation was based on the implemented flow visualization and provided an overview for exploring the data by epidemiologists. In this way, a more scalable visualization delivered the ability to support the creation of hypotheses by finding relationships of interest.