Information visualization: perception for design
Information visualization: perception for design
TreeJuxtaposer: scalable tree comparison using Focus+Context with guaranteed visibility
ACM SIGGRAPH 2003 Papers
FaThumb: a facet-based interface for mobile search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
FacetMap: A Scalable Search and Browse Visualization
IEEE Transactions on Visualization and Computer Graphics
Beyond visual acuity: the perceptual scalability of information visualizations for large displays
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
ManyEyes: a Site for Visualization at Internet Scale
IEEE Transactions on Visualization and Computer Graphics
Glimmer: Multilevel MDS on the GPU
IEEE Transactions on Visualization and Computer Graphics
A Novel Visualization Technique for Electric Power Grid Analytics
IEEE Transactions on Visualization and Computer Graphics
The scalable reasoning system: lightweight visualization for distributed analytics
Information Visualization
Data transformations and representations for computation and visualization
Information Visualization
Scalable, robust visualization of very large trees
EUROVIS'05 Proceedings of the Seventh Joint Eurographics / IEEE VGTC conference on Visualization
Data transformations and representations for computation and visualization
Information Visualization
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The fundamental problem that we face is that a variety of large-scale problems in security, public safety, energy, ecology, health care and basic science all require that we process and understand increasingly vast amounts and variety of data. There is a growing impedance mismatch between data size/complexity and the human ability to understand and interact with data. Visual analytic tools are intended to help reduce that impedance mismatch by using analytic tools to reduce the amount of data that must be viewed, and visualization tools to help understand the patterns and relationships in the reduced data. But visual analytic tools must address a variety of scalability issues if they are to succeed. In this paper, we characterize the scalability and complexity issues in visual analytics. We discuss some highlights on progress that has been made in the past 5 years, as well as key areas where more progress is needed.