Publication Abstract
EVITA - Efficient Visualization and Interrogation of Tera-Scale Data
Machiraju, R., Fowler, J. E., Thompson, D., Schroeder, W., & Soni, B. (2001). EVITA - Efficient Visualization and Interrogation of Tera-Scale Data. In R. L. Grossman, C. Kamath, P. Kegelmeyer, V. Kumar, and R.R. Namburu (Eds.), Data Mining for Scientific and Engineering Applications. New York: Kluwer Academic Publishers. 257-279.
Abstract
Large-scale
computational simulations of physical phenomena produce data of
unprecedented size (terabyte and petabyte range). Unfortunately, development of
appropriate data management and visualization techniques has not kept pace with
the growth in size and complexity of such data sets. To address these issues, we
are developing a prototype, integrated system (EVITA) to facilitate exploration
of terascale
data sets. The cornerstone of the EVITA system is a representational
scheme that allows ranked access to macroscopic features in the data set. The
data and grid are transformed using wavelet techniques while a feature-detection
algorithm is used to identify and rank contextually significant features directly in
the wavelet domain. The most significant parts of the data set are thus available
for detailed examination in a progressive fashion. The work presented here is
similar in essence to much of the work in the traditional data mining
community.
We first describe the basic system and follow with a discussion of ongoing
work, focusing on efforts in multi-scale feature detection and progressive access.
Finally, we demonstrate the system for a two-dimensional
vector field derived
from an oceanographic data set.