IEEE Trans Vis Comput Graph. 2019 Sep;25(9):2853-2872. doi: 10.1109/TVCG.2018.2853721. Epub 2018 Jul 6.
Over the last decade, ensemble visualization has witnessed a significant development due to the wide availability of ensemble data, and the increasing visualization needs from a variety of disciplines. From the data analysis point of view, it can be observed that many ensemble visualization works focus on the same facet of ensemble data, use similar data aggregation or uncertainty modeling methods. However, the lack of reflections on those essential commonalities and a systematic overview of those works prevents visualization researchers from effectively identifying new or unsolved problems and planning for further developments. In this paper, we take a holistic perspective and provide a survey of ensemble visualization. Specifically, we study ensemble visualization works in the recent decade, and categorize them from two perspectives: (1) their proposed visualization techniques; and (2) their involved analytic tasks. For the first perspective, we focus on elaborating how conventional visualization techniques (e.g., surface, volume visualization techniques) have been adapted to ensemble data; for the second perspective, we emphasize how analytic tasks (e.g., comparison, clustering) have been performed differently for ensemble data. From the study of ensemble visualization literature, we have also identified several research trends, as well as some future research opportunities.
在过去的十年中，由于集合数据的广泛可用性，以及来自各种学科的不断增长的可视化需求，集合可视化技术得到了显著发展。从数据分析的角度来看，可以观察到许多集合可视化工作集中在集合数据的同一方面，使用类似的数据聚合或不确定性建模方法。然而，缺乏对这些基本共性的反思，以及对这些工作的系统概述，使得可视化研究人员无法有效地确定新的或未解决的问题，并为进一步的发展做出计划。在本文中，我们从整体角度对集合可视化进行了调查。具体来说，我们研究了最近十年的集合可视化工作，并从两个角度对它们进行了分类：（1）他们提出的可视化技术；（2）他们涉及的分析任务。对于第一个角度，我们专注于详细阐述传统的可视化技术（例如，曲面、体可视化技术）如何适应集合数据；对于第二个角度，我们强调如何针对集合数据执行不同的分析任务（例如，比较、聚类）。通过对集合可视化文献的研究，我们还确定了几个研究趋势，以及一些未来的研究机会。
