Technology Category
Data Mining & Knowledge Management: Data Visualization & SummarizationKnowledge Representation and Reasoning: Diagnosis and Abductive ReasoningHumans and AI: Game Design — Procedural Content Generation & Storytelling
ProVega: A Grammar to Ease the Prototyping, Creation, and Reproducibility of Progressive Data Analysis and Visualization Solutions
🤖 AI Summary
Progressive data analysis and visualization (PDAV) remains challenging to reproduce due to implementation complexity and a lack of standardization. This work proposes ProVega, the first declarative grammar specifically designed for PDAV, extending Vega-Lite to uniformly support chunking strategies including data chunking, process chunking, and hybrid approaches. Accompanying this grammar is Pro-Ex, an integrated editor enabling rapid prototyping and interactive analysis. The proposed framework substantially lowers the development barrier while enhancing reproducibility and efficiency. Empirical evaluation demonstrates successful reproduction of PDAV cases from 11 prior studies, with high-fidelity results validated by 39 users and further confirmed through expert interviews attesting to its practical utility and effectiveness in real-world analytical tasks.
Technology Category
Application Category
📝 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
Problem
Research questions and friction points this paper is trying to address.
Progressive Data Analysis
Visualization
Reproducibility
Prototyping
Interactivity
Innovation
Methods, ideas, or system contributions that make the work stand out.
ProVega
Progressive Data Analysis and Visualization
Vega-Lite-based grammar
Pro-Ex editor
reproducibility
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