Homogeneity guided probabilistic data summaries for analysis and visualization of large-scale data sets
Homogeneity guided probabilistic data summaries for analysis and visualization of large-scale data sets
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用于大规模数据集分析和可视化的同质性引导概率数据摘要
DOI：
10.1109/pacificvis.2017.8031585
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
J. Ahrens
中科院分区：
文献类型：
--
作者：
Soumya Dutta;J. Woodring;Han;Jen‐Ping Chen;J. Ahrens
高分辨率模拟数据集提供了大量的信息，应用科学家需要对这些信息进行探索，以增强对各种现象的理解。由于数据集的极端大小，使用原始数据集的可视化分析技术通常是昂贵的。但是，交互式分析和可视化对于大数据分析至关重要，因为科学家可以专注于重要数据并快速做出关键决策。为了方便高效的数据挖掘和可视化，我们提出了一种新的基于区域的统计数据汇总方案。与现有的统计摘要技术相比，我们的方法在质量上更优越，具有更紧凑的表示，降低了总体存储成本。我们提出的方法的定量和视觉效果通过几个数据集以及一个极端尺度流动模拟的原位应用研究来证明。
High-resolution simulation data sets provide plethora of information, which needs to be explored by application scientists to gain enhanced understanding about various phenomena. Visual-analytics techniques using raw data sets are often expensive due to the data sets' extreme sizes. But, interactive analysis and visualization is crucial for big data analytics, because scientists can then focus on the important data and make critical decisions quickly. To assist efficient exploration and visualization, we propose a new region-based statistical data summarization scheme. Our method is superior in quality, as compared to the existing statistical summarization techniques, with a more compact representation, reducing the overall storage cost. The quantitative and visual efficacy of our proposed method is demonstrated using several data sets along with an in situ application study for an extreme-scale flow simulation.