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We describe two techniques for rendering isosurfaces in multiresolution volume data so that the uncertainty (error) in the data is shown in the visualization. In general the visualization of uncertainty in data is difficult, but the nature of isosurface rendering makes it amenable to an effective solution. In addition to showing the error in the data used(More)
Most caching and prefetching research does not take advantage of prior knowledge of access patterns, or does not adequately address the storage issues inherent with multidimensional scientific data. Armed with an access pattern specified as an iteration over a multidimensional array stored in a disk file, we use prefetching to greatly reduce the number of(More)
We describe two techniques for rendering isosurfaces in multiresolution volume data such that the uncertainty (error) in the data is shown in the resulting visualization. In general the visualization of uncertainty in data is difficult, but the nature of isosurface rendering makes it amenable to an effective solution. In addition to showing the error in the(More)
Modern dataset sizes present major obstacles to understanding and interpreting the significant underlying phenomena represented in the data. There is a critical need to support scientists in the process of interactive exploration of these very large data sets. Using multiple resolutions of the data set (multiresolution), the scientist can identify(More)
Due to the increasing quality of instruments and availability of computational resources, the size of spatial scientific datasets has been steadily increasing. However, much of the research on efficient storage and access to spatial datasets has focused on large multidimensional arrays. In contrast, unstructured datasets consisting of collections of(More)
1 Introduction New data gathering and data generation tools have created an explosion in the amount of data available to scientists. The existence of such large amounts of data provides opportunities that have not previously been possible, but the dataset sizes present major obstacles to understanding and interpreting the significant underlying phenomena(More)
1 Visualization of multidimensional data presents special challenges for the design of efficient out-of-core data access. Elements that are nearby in the visualization may not be nearby in the underlying data file, which can severely tax the operating system's disk cache. The Granite Scientific Database System can address these problems because it is aware(More)
Although processing speed, storage capacity and network bandwidth are steadily increasing, network latency remains a bottleneck for scientists accessing large remote data sets. This problem is most acute with n-dimensional data. Grid researchers have only recently begun to develop tools for efficient remote access to n-dimensional data sets. Within the(More)