ProVega: A Grammar to Ease the Prototyping, Creation, and Reproducibility of Progressive Data Analysis and Visualization Solutions
This addresses the problem of high implementation barriers for researchers and practitioners in data visualization, though it is incremental as it builds on existing Vega-Lite.
The authors tackled the difficulty of implementing and reproducing Progressive Data Analysis and Visualization (PDAV) solutions by introducing ProVega, a grammar that simplifies instrumentation, and Pro-Ex, an editor for creating progressive solutions, validated by reimplementing 11 exemplars with 39 users and an expert study.
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