ABSTRACT
Advanced visualization systems have been widely adopted by decision makers for dealing with problems involving spatial, temporal and multi-dimensional features. While these systems tend to provide reasonable support for particular paradigms, domains, and data types, they are very weak when it comes to supporting multi-paradigm, multi-domain problems that deal with complex spatio-temporal multi-dimensional data. This has led to visualizations that are context insensitive, data dense, and sparse in intelligence. There is a crucial need for visualizations that capture the essence of the relevant information in limited visual spaces allowing decision makers to take better decisions with less effort and time. To address these problems and issues, we propose a visual decision making process that increases the intelligence density of information provided by visualizations. To support this we propose a mechanism by which one could judge the intelligence density of visualizations. Furthermore, we propose and implement a framework and architecture to support the above process in a manner that is independent of data, domain, and paradigm. The system allows decision makers to create, manipulate, layer and view visualizations flexibly enabling the increase in the density of intelligence that they provide.
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Index Terms
- Visual intelligence density: definition, measurement, and implementation
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