Supporting the interactive exploration of large datasets is a popular and challenging use case for data management systems. Traditionally, the interface and the back-end system are built and optimized separately, and interface design and system optimization require different skill sets that are difficult for one person to master. To enable analysts to focus on visualization design, we contribute VegaPlus, a system that automatically optimizes interactive dashboards to support large datasets. To achieve this, VegaPlus leverages two core ideas. First, we introduce an optimizer that can reason about execution plans in Vega, a back-end DBMS, or a mix of both environments. The optimizer also considers how user interactions may alter execution plan performance, and can partially or fully rewrite the plans when needed. Through a series of benchmark experiments on seven different dashboard designs, our results show that VegaPlus provides superior performance and versatility compared to standard dashboard optimization techniques.

翻译：支持大规模数据集的交互式探索是数据管理系统面临的一项既流行又具有挑战性的应用场景。传统上，用户界面和后端系统是分别构建和优化的，而界面设计与系统优化所需的不同技能集难以由同一个人掌握。为使分析人员能专注于可视化设计，我们提出了VegaPlus系统，该系统可自动优化交互式仪表板以支持大规模数据集。为实现这一目标，VegaPlus采用了两项核心思想。首先，我们引入了一个优化器，它能够解析Vega、后端DBMS或两者混合环境中的执行计划。该优化器还会考虑用户交互如何影响执行计划性能，并能在必要时部分或完全重写计划。通过七种不同仪表板设计的基准实验，结果表明，与标准仪表板优化技术相比，VegaPlus在性能和通用性方面均表现卓越。