Enhancing Computational Notebooks with Code+Data Space Versioning

要旨

There is a gap between how people explore data and how Jupyter-like computational notebooks are designed. People explore data nonlinearly, using execution undos, branching, and/or complete reverts, whereas notebooks are designed for sequential exploration. Recent works like ForkIt are still insufficient to support these multiple modes of nonlinear exploration in a unified way. In this work, we address the challenge by introducing two dimensional code+data space versioning for computational notebooks and verifying its effectiveness using our prototype system, Kishuboard, which integrates with Jupyter. By adjusting code and data knobs, users of Kishuboard can intuitively manage the state of computational notebooks in a flexible way, thereby achieving both execution rollbacks and checkouts across complex multi-branch exploration history. Moreover, this two-dimensional versioning mechanism can easily be presented along with a friendly one-dimensional history. Human subject studies indicate that Kishuboard significantly enhances user productivity in various data science tasks.

著者
Hanxi Fang
University of Illinois at Urbana-Champaign, Champaign, Illinois, United States
Supawit Chockchowwat
University of Illinois Urbana-Champaign, Urbana, Illinois, United States
Hari Sundaram
University of Illinois, Urbana, Illinois, United States
Yongjoo Park
University of Illinois at Urbana-Champaign, Champaign, Illinois, United States
DOI

10.1145/3706598.3714141

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714141

動画

会議: CHI 2025

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

セッション: Coding and Development

G418+G419
7 件の発表
2025-04-29 23:10:00
2025-04-30 00:40:00
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