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

翻译：现代数据分析需要处理大规模数据集的速度。渐进式数据分析与可视化（PDAV）作为应对该问题的学科应运而生，在保持交互性的同时以可控精度提供快速响应时间。然而，其实现与复现仍存在困难。为降低这一门槛，我们提出ProVega——一种基于Vega-Lite的语法，可简化从简单可视化到复杂视觉环境的PDAV工具化过程。伴随该语法，我们引入Pro-Ex编辑器，旨在简化渐进式解决方案的创建与分析流程。我们通过复现文献中的11个范例（经39名用户验证忠实性）验证了ProVega，并展示了其支持多种渐进式方法（包括数据分块、过程分块及混合分块）的能力。专家用户研究证实了ProVega与Pro-Ex环境在实际任务中的有效性。ProVega、Pro-Ex及所有相关材料可通过https://github.com/XAIber-lab/provega获取。