Glatter Markus, Mollenhour Colin, Huang Jian, Gao Jinzhu
The University of Tennessee, USA.
IEEE Trans Vis Comput Graph. 2006 Sep-Oct;12(5):1291-8.
Volumetric datasets with multiple variables on each voxel over multiple time steps are often complex, especially when considering the exponentially large attribute space formed by the variables in combination with the spatial and temporal dimensions. It is intuitive, practical, and thus often desirable, to interactively select a subset of the data from within that high-dimensional value space for efficient visualization. This approach is straightforward to implement if the dataset is small enough to be stored entirely in-core. However, to handle datasets sized at hundreds of gigabytes and beyond, this simplistic approach becomes infeasible and thus, more sophisticated solutions are needed. In this work, we developed a system that supports efficient visualization of an arbitrary subset, selected by range-queries, of a large multivariate time-varying dataset. By employing specialized data structures and schemes of data distribution, our system can leverage a large number of networked computers as parallel data servers, and guarantees a near optimal load-balance. We demonstrate our system of scalable data servers using two large time-varying simulation datasets.
在多个时间步长上每个体素具有多个变量的体数据集通常很复杂，特别是当考虑由变量与空间和时间维度相结合形成的指数级大的属性空间时。直观、实用且因此通常是可取的做法是，在该高维值空间内交互式选择数据子集以进行高效可视化。如果数据集足够小可以完全存储在内存中，这种方法很容易实现。然而，对于数百GB及以上大小的数据集，这种简单的方法变得不可行，因此需要更复杂的解决方案。在这项工作中，我们开发了一个系统，该系统支持对大型多变量时变数据集通过范围查询选择的任意子集进行高效可视化。通过采用专门的数据结构和数据分布方案，我们的系统可以利用大量联网计算机作为并行数据服务器，并保证接近最优的负载平衡。我们使用两个大型时变模拟数据集展示了我们的可扩展数据服务器系统。
