Composition and Configuration Patterns in Multiple-View Visualizations

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