R. Machiraju, J. E. Fowler, D. Thompson, B. Soni, and W. Schroeder, "EVITA - Efficient Visualization and Interrogation of Tera-Scale Data," in Data Mining for Scientific and Engineering Applications, R. L. Grossman, C. Kamath, P. Kegelmeyer, V. Kumar, and R. R. Namburu, Eds., chapter 15, pp. 257-279, Kluwer Academic Publishers, Norwell, MA, 2001.
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 datasets. To address these issues, we are developing a prototype, integrated system (EVITA) to facilitate exploration of tera-scale datasets. The cornerstone of the EVITA system is a representational scheme that allows ranked access to macroscopic features in the dataset. 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 dataset 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 multiscale feature detection and progressive access. Finally, we demonstrate the system for a two-dimensional vector field derived from an oceanographic dataset.
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