ProVega: A Grammar to Ease the Prototyping, Creation, and Reproducibility of Progressive Data Analysis and Visualization Solutions

TL;DR

ProVega is a Vega-Lite-based grammar and editor designed to simplify the development and reproducibility of progressive data analysis and visualization solutions, validated through multiple exemplars and user studies.

Contribution

It introduces ProVega and Pro-Ex, tools that lower barriers to implementing and reproducing progressive data analysis and visualization methods.

Findings

Successfully reimplemented 11 literature exemplars with ProVega.

Supported various progressive methods including data-chunking, process-chunking, and mixed-chunking.

User study confirmed effectiveness in real-world tasks.

Abstract

Modern data analysis requires speed for massive datasets. Progressive Data Analysis and Visualization (PDAV) emerged as a discipline to address this problem, providing fast response times while maintaining interactivity with controlled accuracy. Yet it remains difficult to implement and reproduce. To lower this barrier, we present ProVega, a Vega-Lite-based grammar that simplifies PDAV instrumentation for both simple visualizations and complex visual environments. Alongside it, we introduce Pro-Ex, an editor designed to streamline the creation and analysis of progressive solutions. We validated ProVega by reimplementing 11 exemplars from the literature-verified for fidelity by 39 users-and demonstrating its support for various progressive methods, including data-chunking, process-chunking, and mixed-chunking. An expert user study confirmed the efficacy of ProVega and the Pro-Ex environment in real-world tasks. ProVega, Pro-Ex, and all related materials are available at https://github.com/XAIber-lab/provega