This project investigates the use of hierarchical methods for accessing and visualizing large and complex multi- dimensional data sets. Visualization can provide an efficient and powerful means of exploring such data However, most visualization methods are developed for moderately sized non-hierarchial data sets that can be imaged acceptably fast on graphics workstations. In practice, there are many data sets that cannot be effectively explored using these methods. The project is developing and implementing visualization approches designed for very large, multi-dimensional data sets over a variety of grid types. Such data sets present significant problems in the management of time and space resources, which will be addressed by this research. The unifying theme will be the use of hierarchical data structures to manage both resources effectively. Addressing time efficiency, techniques are being developed for the selective traversal and rapid interactive imaging of the data. For space efficiency, various data models, such as wavelets, are investigated to achieve compression, while being directly amendable to visualization. These hierarchies are being integrated with existing visualization techniques for isosurface extraction, direct volume rendering, and flow visualization, many of which have already been developed at UC Santa Cruz and elsewhere for non-hierarchial data. Techniques for rectilinear, structured, and unstructured grids are studied. Parallelization of these methods of SIMD and MIMD machines is also is being explored. This research is developing new algorithms for visualization of large, complex data sets, as well as making software systems available for use by scientists. Software is modular to facilitate integration with existing visualization packages. While the main objective is visualization, the hierarchical methods developed will also be of use for any data analysis hampered by access problems in dealing with very large data sets.
