发表机构
University of California San Diego; National University of Singapore; Tableau Research（加州大学圣地亚哥分校; 新加坡国立大学; Tableau研究）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MuTable通过将表变换作为可组合、可复用的修饰符，支持原位数据探索，减少上下文切换，增强用户构建可视化的自主权。
AI 中文摘要
表格是数据工作的核心，支持精确查找和完整细节，但在概览和模式发现任务中可能有所局限。为此，人们创建可视化以获取更丰富的感知支持。在实践中，在表格和图表之间切换通常需要维护并行表示，导致上下文切换和额外的协调工作。基于先前的混合表格-可视化系统，我们提出了MuTable，一个原型系统，将变换具体化为持久、可组合且可复用的修饰符，以支持原位数据探索。用户可以重塑表格，同时在其问题演变时保留并调整中间形式。对八位数据工作者的专家访谈表明，MuTable能够支持表示之间的协调、快速探索，并赋予用户在构建可视化时更大的自主权，作为一个低承诺的探索空间。
英文摘要
Tables are central to data work to support precise lookup and full detail, but they can be limiting for overview and pattern-finding tasks. Visualizations are then created to gain richer perceptual support. In practice, moving between tables and charts often requires maintaining parallel representations, introducing context switching, and extra coordination work. Building on prior hybrid table-visualization systems, we present MuTable, a prototype that reifies transformations as persistent, composable, and reusable modifiers to support in-situ data exploration. Users can reshape the table while retaining and adapting intermediate forms as their questions evolve. An expert interview with eight data workers suggests that MuTable can support coordination between representations, rapid exploration, and greater user agency in constructing visualizations, as a low-commitment exploration space.
CommentsTo be published in UIST 26
