Hierarchical Exploration and Visualization of Large and Complex Multi-Dimensional Data Sets

该项目研究使用分层方法来访问和可视化大型和复杂的多维数据集。可视化可以提供探索这种数据的有效和强大的手段，然而，大多数可视化方法是针对中等大小的非分层数据集开发的，这些数据集可以在图形工作站上以可接受的速度成像。在实践中，有许多数据集不能使用这些方法有效地探索。该项目正在开发和实施为各种网格类型上的超大型多维数据集设计的可视化方法。这样的数据集在时间和空间资源的管理中提出了重大问题，本研究将解决这一问题。统一的主题将是使用分层数据结构来有效地管理这两种资源。为了提高时间效率，正在开发数据的选择性遍历和快速交互成像技术。为了提高空间效率，研究了各种数据模型，如小波，以实现压缩，同时可直接修改为可视化。这些分层结构正在与现有的可视化技术集成，用于等值面提取、直接体绘制和流动可视化，其中许多技术已经在加州大学圣克鲁斯分校和其他地方为非分层数据开发。研究了直线网格、结构化网格和非结构化网格的技术。SIMD和MIMD机器的这些方法的并行化也在探索中。这项研究正在开发新的算法，用于可视化大型、复杂的数据集，并使软件系统可供科学家使用。软件是模块化的，以促进与现有可视化包的集成。虽然主要目标是可视化，但开发的分层方法也将用于在处理非常大的数据集时因访问问题而受阻的任何数据分析。

This project investigates the use of hierarchical methods for accessing and visualizing large and complex multi- dimensional data sets. Visualization can provide an efficient and powerful means of exploring such data However, most visualization methods are developed for moderately sized non-hierarchial data sets that can be imaged acceptably fast on graphics workstations. In practice, there are many data sets that cannot be effectively explored using these methods. The project is developing and implementing visualization approches designed for very large, multi-dimensional data sets over a variety of grid types. Such data sets present significant problems in the management of time and space resources, which will be addressed by this research. The unifying theme will be the use of hierarchical data structures to manage both resources effectively. Addressing time efficiency, techniques are being developed for the selective traversal and rapid interactive imaging of the data. For space efficiency, various data models, such as wavelets, are investigated to achieve compression, while being directly amendable to visualization. These hierarchies are being integrated with existing visualization techniques for isosurface extraction, direct volume rendering, and flow visualization, many of which have already been developed at UC Santa Cruz and elsewhere for non-hierarchial data. Techniques for rectilinear, structured, and unstructured grids are studied. Parallelization of these methods of SIMD and MIMD machines is also is being explored. This research is developing new algorithms for visualization of large, complex data sets, as well as making software systems available for use by scientists. Software is modular to facilitate integration with existing visualization packages. While the main objective is visualization, the hierarchical methods developed will also be of use for any data analysis hampered by access problems in dealing with very large data sets.