TableCanoniser: Interactive Grammar-Powered Transformation of Messy, Non-Relational Tables to Canonical Tables

要旨

TableCanoniser is a declarative grammar and interactive system for constructing relational tables from messy tabular inputs such as spreadsheets. We propose the concept of axis alignment to categorise input types and characterise the expanded scope of our system relative to existing tools. The declarative grammar consists of match conditions, which specify repeating patterns of input cells, and extract operations, which specify how matched values map to the output table. In the interactive interface, users can specify match and extract patterns by interacting with an input table, or author more advanced specifications in the coding panel. To refine and verify specifications, users interact with grammar-based provenance visualisations such as linked highlighting of input and output values, tree-based visualisation of matching patterns, and a mini-map overview of matched instances of patterns with annotations showing where cells are extracted to. We motivate and illustrate our work with real-world usage scenarios and workflows.

受賞
Honorable Mention
著者
Kai Xiong
Zhejiang University, Hangzhou, Zhejiang, China
Cynthia A. Huang
Monash University, Melbourne, Victoria, Australia
Michael Wybrow
Monash University, Melbourne, VIC, Australia
Yingcai Wu
Zhejiang University, Hangzhou, Zhejiang, China
DOI

10.1145/3706598.3714321

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714321

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Playing with Data

Annex Hall F206
7 件の発表
2025-04-30 20:10:00
2025-04-30 21:40:00
日本語まとめ
読み込み中…