Interactive visualization of very large data sets remains a challenging problem to the visualization community.
One promising solution involves using adaptive resolution representations of the data. In this model, important
regions of data are identified using reconstructive error analysis and are shown in higher detail. During the
visualization, regions with higher error are rendered with high resolution data, while areas of low error are
rendered at a lower resolution. We have developed a new dynamic adaptive resolution rendering algorithm along
with software support libraries. These libraries are designed to extend the VisIt visualization environment by
adding support for adaptive resolution data. VisIt supports domain decomposition of data, which we use to
define our AR representation. We show that with this model, we achieve performance gains while maintaining
error tolerances specified by the scientist.
Visualization and analysis of very large datasets remains a significant challenge to the visualization community.
Scientists have tried various techniques to deal with large data. Multiresolution data models reduce the size of the data using techniques such as mesh decimation, wavelet transformation, or data compression. The low resolution representation raises issues concerning the authenticity of the data at its resolution level. We address this issue by presenting our extensions to the VisIt visualization environment that enable the scientist to visualize
both multiresolution data and the uncertainty information associated with the lower resolution representations of the data.
We present an application case study for visualizing large data sets of time series spatial data. Our application is built on a flexible, object oriented framework that supports the visualization of dynamic internal wave propagation in the earth's tropopause. Our data model uses a multiresolution hierarchy that integrates spatial and temporal components. The data also includes error information at each level of the hierarchy. The application provides the scientist with tools necessary to examine, query, and interact with visualizations of data of interest.