Search

One of the central challenges in modern science is the need to quickly derive knowledge and understanding from large, complex collections of data. We present a new approach that deals with this challenge by combining and extending techniques from high performance visual data analysis and scientific data management. This approach is demonstrated within the context of gaining insight from complex, time-varying datasets produced by a laser wakefield accelerator simulation. Our approach leverages histogram-based parallel coordinates for both visual information display as well as a vehicle for guiding a data mining operation. Data extraction and subsetting are implemented with state-of-the-art index/query technology. This approach, while applied here to accelerator science, is generally applicable to a broad set of science applications, and is implemented in a production-quality visual data analysis infrastructure. We conduct a detailed performance analysis and demonstrate good scalability on a distributed memory Cray XT4 system. © 2008 IEEE.

We present an octree-based approach for isosurface extraction from large volumetric scalar-valued data. Given scattered points with associated function values, we impose an octree structure of relatively low resolution. Octree construction is controlled by original data resolution and cell-specific error values. For each cell in the octree, we compute an average function value and additional statistical data for the original points inside the cell. Once a specific isovalue is specified, we adjust the initial octree by expanding its leaves based on a comparison of statistics with the isovalue. We tetrahedrize the centers of the octree's cells to determine tetrahedral meshes decomposing the entire spatial domain of the data, including a possibly specific region of interest (ROI). Extracted isosurfaces are crack-free inside an ROI, but cracks can appear at the boundary of an ROI. The inital isosurface is an approximation of the exact one, but its quality suffices for a viewer to identify an ROI where more accuracy is desirable. In the refinement process, we refine affected octree nodes and update the triangulation locally to produce better isosurface representations. This adaptive and user-driven refinement provides a means for interactive data exploration via real-time and local isosurface extraction.