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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.


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.