Hierarchical Approaches for the Visualization of Massive Scientific Data
Thursday, April 26, 2001 - 11:00am - 12:00pm
Bernd Hamann (University of California)
One of the most challenging and important problems that the science and engineering communities are facing today --- and even more so in the future --- are representing, visualizing, and interpreting very large data sets. Such data sets commonly result from computer simulations of complex physical phenomena (e.g., computational physics, climate modeling, ocean modeling) or from high-resolution imaging (e.g., satellite imaging, medical imaging). The technology currently used to represent massive data sets is inappropriate for interactive and efficient data analysis and visualization. It is impossible for a user of a visualization system to navigate through a data set consisting of several million (or billion) data points and analyze the data set entirely. In this talk, I will present various ideas that seem to be promising in the context of overcoming some of the problems associated with the visualization of very large data sets. I will emphasize the necessity to bring together approaches from approximation theory; geometric modeling (splines) and grid generation; computational geometry (tesselations); optimization (simulated annealing); and other appropriate fields. I will point out various avenues for representing massive data sets using hierarchical approaches that facilitate massive data set visualization and exploration.