IEEE VIS 2024 Content: Loops: Leveraging Provenance and Visualization to Support Exploratory Data Analysis in Notebooks

Loops: Leveraging Provenance and Visualization to Support Exploratory Data Analysis in Notebooks

Klaus Eckelt - Johannes Kepler University Linz, Linz, Austria

Kiran Gadhave - University of Utah, Salt Lake City, United States

Alexander Lex - University of Utah, Salt Lake City, United States

Marc Streit - Johannes Kepler University Linz, Linz, Austria

Room: Bayshore V

2024-10-16T18:09:00ZGMT-0600Change your timezone on the schedule page
2024-10-16T18:09:00Z
Exemplar figure, described by caption below
Loops tracks and visualizes the provenance of computational notebooks. Compact and detailed visualizations of the notebook's history trace the evolution of the notebook over time and highlight differences between versions. Loops visualizes the provenance of code, markdown, tables, visualizations, and images and can explicitly encode their differences.
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Keywords

Comparative visualization, computational notebooks, provenance, data science

Abstract

Exploratory data science is an iterative process of obtaining, cleaning, profiling, analyzing, and interpreting data. This cyclical way of working creates challenges within the linear structure of computational notebooks, leading to issues with code quality, recall, and reproducibility. To remedy this, we present Loops, a set of visual support techniques for iterative and exploratory data analysis in computational notebooks. Loops leverages provenance information to visualize the impact of changes made within a notebook. In visualizations of the notebook provenance, we trace the evolution of the notebook over time and highlight differences between versions. Loops visualizes the provenance of code, markdown, tables, visualizations, and images and their respective differences. Analysts can explore these differences in detail in a separate view. Loops not only makes the analysis process transparent but also supports analysts in their data science work by showing the effects of changes and facilitating comparison of multiple versions. We demonstrate our approach's utility and potential impact in two use cases and feedback from notebook users from various backgrounds. This paper and all supplemental materials are available at https://osf.io/79eyn.