Set systems are used to model data that naturally arises in many contexts: social networks have communities, musicians have genres, and patients have symptoms. Visualizations that accurately reflect the information in the underlying set system make it possible to identify the set elements, the sets themselves, and the relationships between the sets. In static contexts, such as print media or infographics, it is necessary to capture this information without the help of interactions. With this in mind, we consider three different systems for medium-sized set data, LineSets, EulerView, and MetroSets, and report the results of a controlled human-subjects experiment comparing their effectiveness. Specifically, we evaluate the performance, in terms of time and error, on tasks that cover the spectrum of static set-based tasks. We also collect and analyze qualitative data about the three different visualization systems. Our results include statistically significant differences, suggesting that MetroSets performs and scales better.

中文翻译：

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关于抽象集可视化的可读性

集合系统用于对在许多情况下自然产生的数据进行建模：社交网络有社区，音乐家有流派，患者有症状。准确反映底层集合系统中信息的可视化使得识别集合元素、集合本身以及集合之间的关系成为可能。在静态环境中，例如印刷媒体或信息图表，有必要在没有交互帮助的情况下捕获这些信息。考虑到这一点，我们考虑了三种不同的中型集合数据系统 LineSets、EulerView 和 MetroSets，并报告了比较其有效性的受控人类受试者实验的结果。具体来说，我们在涵盖基于静态集的任务范围的任务上评估时间和错误方面的性能。我们还收集和分析有关三种不同可视化系统的定性数据。我们的结果包括统计上的显着差异，表明 MetroSets 的性能和扩展性更好。