AI 中文总结
该研究旨在为边缘和轨迹捆绑构建任务分类法，通过收集相关论文语料库，得出范围与动作交叉的矩阵分类法，揭示了捆绑对任务的启用和禁用情况，其成果以补充材料形式发布。
AI 中文摘要
边缘捆绑通过聚合相似边缘减少视觉混乱，但从业者缺乏用于推理捆绑可视化所支持任务的结构化词汇表。为填补这一空白，我们收集了102篇论文的语料库，其中49篇包含明确的捆绑任务，涵盖节点链接图、地理轨迹集和平行坐标图。由此得出一个任务分类法，组织为范围（元素、捆绑、全局、多视图）与动作（验证、识别、表征、量化、比较、评估）交叉的矩阵，并在三种表示类型中实例化。我们表明捆绑同时启用了一些任务（捆绑级和全局推理）并禁用了其他任务（元素级精度），这是现有任务框架未捕捉到的二元性。我们编码的语料库和分类法作为补充材料在OSF上发布。
英文摘要
Edge bundling reduces visual clutter by aggregating similar edges, yet practitioners lack a structured vocabulary for reasoning about the tasks that bundled visualizations support. Such a vocabulary is needed both to evaluate the general utility of bundling and to compare different bundling approaches. We address this gap by assembling a corpus of 102 papers, 49 of which contain explicit bundling tasks, spanning node-link diagrams, geographic trail sets, and parallel coordinate plots. From this corpus, we derive a task taxonomy organized as a matrix of scope (Element, Bundle, Global, Multi-view) crossed with action (Verify, Identify, Characterize, Quantify, Compare, Assess), instantiated across the three representation types. We show that bundling simultaneously enables tasks (bundle-level and global reasoning) and disables others (element-level precision), a duality not captured by existing task frameworks. Our coded corpus and taxonomy are released as supplemental material on OSF (osf.io/23r67).
CommentsAccepted for presentation at IEEE VIS (Short Paper)
