VisBricks: Multiform Visualization of Large, Inhomogeneous Data
VisBricks: Multiform Visualization of Large, Inhomogeneous Data
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DOI：
10.1109/tvcg.2011.250
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发表时间：
2011-12-01
影响因子：
5.2
通讯作者：
Schmalstieg, Dieter
中科院分区：
文献类型：
--
作者：
Lex, Alexander;Schulz, Hans-Joerg;Schmalstieg, Dieter
大量的真实世界数据经常表现出不均质性：纵向上表现为相关或独立维度的形式，水平上表现为聚集或分散的数据项的形式。本质上，这些不一致性形成了研究人员试图发现和理解的数据中的模式。复杂的统计方法可以揭示这些模式，然而，对其结果的可视化大多仍然是以一种一视同仁的方式进行的。相比之下，我们的新可视化方法VisBricks承认数据的不均质性，并且需要适合不同数据子集的不同特征的不同可视化。整个数据集的整体可视化是从较小的可视化拼接在一起的，在每组相互依赖的维度中，每个集群都有一个VisBrick。虽然所有VisBrick的总体印象提供了不同数据组的全面高级概述，但每个VisBrick都独立地显示了它所代表的数据组的详细信息。所有VisBrick之间最先进的刷新和可视链接还允许比较分组和它们之间的数据项分布。本文介绍了VisBricks可视化的概念，讨论了它的设计原理和实现，并通过一个生物医学领域的用例说明了它的有效性。
Large volumes of real-world data often exhibit inhomogeneities: vertically in the form of correlated or independent dimensions and horizontally in the form of clustered or scattered data items. In essence, these inhomogeneities form the patterns in the data that researchers are trying to find and understand. Sophisticated statistical methods are available to reveal these patterns, however, the visualization of their outcomes is mostly still performed in a one-view-fits-all manner. In contrast, our novel visualization approach, VisBricks, acknowledges the inhomogeneity of the data and the need for different visualizations that suit the individual characteristics of the different data subsets. The overall visualization of the entire data set is patched together from smaller visualizations, there is one VisBrick for each cluster in each group of interdependent dimensions. Whereas the total impression of all VisBricks together gives a comprehensive high-level overview of the different groups of data, each VisBrick independently shows the details of the group of data it represents. State-of-the-art brushing and visual linking between all VisBricks furthermore allows the comparison of the groupings and the distribution of data items among them. In this paper, we introduce the VisBricks visualization concept, discuss its design rationale and implementation, and demonstrate its usefulness by applying it to a use case from the field of biomedicine.