AI 中文总结
该研究以数据为中心视角，通过准备表整理133种二维树可视化技术，发现其编码前数据需求的模式集中，为树可视化设计的结构差异分析提供了新方法。
AI 中文摘要
树可视化（TreeVis）技术涵盖多样设计，现有分类法按布局维度、边表示、节点对齐等视觉特征组织这些技术。然而，这种以视觉为中心的视角可能掩盖结构相似性，且难以区分差异源于数据结构还是视觉编码。我们以数据为中心视角研究TreeVis技术，该视角基于视觉编码前的最终数据状态——准备表（Prepared Tables）。我们利用该资源，整理出133种二维技术，并对每种技术编码前所需的对象记录和属性角色进行表征。我们的分析显示，在准备数据层面，该技术集合比视觉阅读所显示的更为集中。这些技术可归为少量反复出现的对象组合和模式。多个TreeVis表示类别的技术共享同一模式，这表明TreeVis设计的诸多表面差异源于视觉表示，而非编码前数据需求的根本不同。因此，准备表模式支持对TreeVis设计间的结构等价性、充分性及差异进行推理。
英文摘要
Tree visualization (TreeVis) techniques span diverse designs. Existing taxonomies organize them by visual characteristics such as layout dimensionality, edge representation, and node alignment. However, this visual-centric perspective can obscure structural similarities and make it difficult to determine whether differences arise from data structures or visual encodings. We investigate TreeVis techniques from a data-centric perspective grounded in Prepared Tables, the final data state prior to visual encoding. Using TreeVis.net, we curate 133 two-dimensional techniques and characterize each by the object records and attribute roles required before encoding. Our analysis shows that the corpus is more concentrated at the prepared-data level than a visual reading would suggest. The techniques collapse to a small set of recurring object combinations and schemas. Many techniques across TreeVis representation categories share the same schema, suggesting that much of the apparent diversity of TreeVis designs lies in visual representation rather than fundamentally different pre-encoding data requirements. Prepared Table schemas therefore support reasoning about structural equivalence, sufficiency, and difference across TreeVis designs.
Comments5 pages, 3 figures. To appear in IEEE VIS 2026 (short paper)
