Although there are a number of visualization systems to choose from when analyzing data, only a few of these allow for the integration of other visualization and analysis techniques. There are even fewer visualization toolkits and frameworks from which one can develop ones own visualization applications. Even within the research community, scientists either use what they can from the available tools or start from scratch to define a program in which they are able to develop new or modified visualization techniques and analysis algorithms. Presented here is a new general-purpose platform for constructing numerous visualization and analysis applications. The focus of this system is the design and experimentation of new techniques, and where the sharing of and integration with other tools becomes second nature. Moreover, this platform supports multiple large data sets, and the recording and visualizing of user sessions. Here we introduce the Universal Visualization Platform (UVP) as a modern data visualization and analysis system.
Several authors have developed automated parameterized visualization generation systems14,15,16. All generate classic
visualizations or combinations of such visualizations. A vector space model of visualization was proposed by Hoffman18,
leading to the development of new visualizations and the concept of interpolating visualizations. These new
visualizations provide alternative representations and insights into data and have been applied successfully in numerous
data analysis problems including gene expression, drug discovery, clinical trials, toxicogenomics, and medical
informatics23. In this paper we elevate this vector space model to include analytic visualizations, ones with tightly
coupled analysis, such as Self-Organizing Maps (SOMs) and Multi-Dimensional Scaling (MDS). We describe our new
model and provide an example interpolation of a SOM and a scatterplot with a simple data set (the Fisher Iris data) and a
more complex and larger one (microarray gene expression data).