Smallset Timelines: A Visual Representation of Data Preprocessing Decisions

TL;DR

The paper introduces Smallset Timelines, a visualization tool that helps data scientists document, reflect on, and communicate data preprocessing decisions effectively, enhancing transparency and reproducibility.

Contribution

It presents a novel visualization method and an R package for creating Smallset Timelines from preprocessing scripts, improving data provenance documentation.

Findings

Visualizes preprocessing decisions through Smallset snapshots

Highlights dataset alterations with color coding

Demonstrates use cases in software defect and income survey data

Abstract

Data preprocessing is a crucial stage in the data analysis pipeline, with both technical and social aspects to consider. Yet, the attention it receives is often lacking in research practice and dissemination. We present the Smallset Timeline, a visualisation to help reflect on and communicate data preprocessing decisions. A "Smallset" is a small selection of rows from the original dataset containing instances of dataset alterations. The Timeline is comprised of Smallset snapshots representing different points in the preprocessing stage and captions to describe the alterations visualised at each point. Edits, additions, and deletions to the dataset are highlighted with colour. We develop the R software package, smallsets, that can create Smallset Timelines from R and Python data preprocessing scripts. Constructing the figure asks practitioners to reflect on and revise decisions as necessary, while sharing it aims to make the process accessible to a diverse range of audiences. We present two case studies to illustrate use of the Smallset Timeline for visualising preprocessing decisions. Case studies include software defect data and income survey benchmark data, in which preprocessing affects levels of data loss and group fairness in prediction tasks, respectively. We envision Smallset Timelines as a go-to data provenance tool, enabling better documentation and communication of preprocessing tasks at large.