Static correlation visualization for large time-varying volume data
Static correlation visualization for large time-varying volume data
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DOI：
10.1109/pacificvis.2011.5742369
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发表时间：
2011-03
期刊：
影响因子：
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通讯作者：
Cheng-Kai Chen;Chaoli Wang;K. Ma;A. Wittenberg
中科院分区：
文献类型：
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作者：
Cheng-Kai Chen;Chaoli Wang;K. Ma;A. Wittenberg
寻找数据之间的相关性是许多科学研究和发现中最重要的任务之一。本文解决了创建静态体积分类的问题，该分类总结了时变多元数据集中的相关性连接。实际上，计算大型 3D 时变多元数据集的所有时间和空间相关性的成本极其昂贵。我们提出了一种基于采样的方法来对相关模式进行分类。我们的采样方案包括三个步骤：从体积中选择重要样本，优先考虑样本对的距离计算，以及用基于样本的相关性来近似基于体积的相关性。我们对样本体素进行分类以产生静态可视化，简洁地总结了所有相关体积之间相对于各种参考位置的连接。我们还研究了采样方案的每个步骤在分类准确性方面引入的误差。领域科学家参与了这项工作并帮助我们选择样本并评估结果。我们的方法通常适用于相关性研究相关的其他科学数据的分析。
Finding correlations among data is one of the most essential tasks in many scientific investigations and discoveries. This paper addresses the issue of creating a static volume classification that summarizes the correlation connection in time-varying multivariate data sets. In practice, computing all temporal and spatial correlations for large 3D time-varying multivariate data sets is prohibitively expensive. We present a sampling-based approach to classifying correlation patterns. Our sampling scheme consists of three steps: selecting important samples from the volume, prioritizing distance computation for sample pairs, and approximating volume-based correlation with sample-based correlation. We classify sample voxels to produce static visualization that succinctly summarize the connection among all correlation volumes with respect to various reference locations. We also investigate the error introduced by each step of our sampling scheme in terms of classification accuracy. Domain scientists participated in this work and helped us select samples and evaluate results. Our approach is generally applicable to the analysis of other scientific data where correlation study is relevant.