Multiple-view visualization (MV) is a layout design technique often employed to help users see a large number of data attributes and values in a single cohesive representation. Because of its generalizability, the MV design has been widely adopted by the visualization community to help users examine and interact with large, complex, and high-dimensional data. However, although ubiquitous, there has been little work to categorize and analyze MVs in order to better understand its design space. As a result, there has been little to no guideline in how to use the MV design effectively. In this paper, we present an in-depth study of how MVs are designed in practice. We focus on two fundamental measures of multiple-view patterns: composition, which quantifies what view types and how many are there; and configuration, which characterizes spatial arrangement of view layouts in the display space. We build a new dataset containing 360 images of MVs collected from IEEE VIS, EuroVis, and PacificVis publications 2011 to 2019, and make fine-grained annotations of view types and layouts for these visualization images. From this data we conduct composition and configuration analyses using quantitative metrics of term frequency and layout topology. We identify common practices around MVs, including relationship of view types, popular view layouts, and correlation between view types and layouts. We combine the findings into a MV recommendation system, providing interactive tools to explore the design space, and support example-based design.

翻译：多视图可视化(MV)是一种布局设计技术,通常用于帮助用户在单一一致的演示中看到大量数据属性和值。由于其可视化社区广泛采用MV设计,以帮助用户检查和与大型、复杂和高维数据互动。不过,虽然无处不在,但为更好地了解其设计空间而对MV进行分类和分析的工作很少。因此,在如何有效使用MV设计方面几乎没有到任何指导。在本文件中,我们深入研究了MV如何在实际中设计。我们侧重于多视图模式的两个基本计量:构成,它量化了哪些视图类型和有多少数据;配置了显示空间的视图布局的空间安排。我们建立了一个包含从IEEE VIS、Eurow Vis和Pacific Vis出版物(2011-2019年)收集的360个MV图像的新数据集。我们对这些可视化图像的视图类型和布局进行了精确的描述。我们用这种数据、模型的配置和图像格式化模型,我们用这种数据类型来对图像进行定量的配置和对比分析。我们用数据、图像格式的配置和格式格式分析,包括各种空间版式的矩阵的矩阵的矩阵的矩阵的配置和结构。