MuTable: Composable and Reusable Table Transformations for In-Situ Data Exploration

2026-09-16

AI Summary

为解决表格和图表间切换的问题，MuTable通过可组合和复用的变换支持原位数据探索。

Abstract

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.

Problem

• tables
• visualizations
• data exploration
• context switching
• coordination work

Innovation

• composable transformations
• in-situ data exploration
• persistent modifiers