Interactive visualization is a common tool for exploring large open-data repositories, where users quickly explore datasets across diverse domains. When it comes to large-scale spatial data, many existing tools rely on server-side rendering to produce small images that can be viewed at the client-side. However, most users prefer client-side rendering that allows quick styling of the data for better visualization experience. This paper presents HiFIVE, a data-management framework for scalable, high-fidelity client-side geospatial visualization. We formalize the visualization-aware tile reduction problem, which captures the trade-off between tile-size and visualization distortion, and prove its NP-hardness. HiFIVE introduces a two-stage solution combining triage and sparsification to selectively prune records, attributes, and values based on information-theoretic and spatial criteria. Experiments demonstrate substantial tile-size reductions while preserving visual fidelity and interactive performance at terabyte scale.

翻译：交互式可视化是探索大型开放数据仓库的常用工具，用户可通过其快速浏览跨领域的多样化数据集。面对大规模空间数据时，现有工具多依赖服务端渲染生成可在客户端查看的小尺寸图像。然而，多数用户更青睐支持快速数据样式配置的客户端渲染方案，以获得更优的可视化体验。本文提出HiFIVE——一个面向可扩展、高保真客户端地理空间可视化的数据管理框架。我们形式化定义了可视化感知的瓦片压缩问题，该问题刻画了瓦片尺寸与可视化失真之间的权衡关系，并证明了其NP难特性。HiFIVE采用包含分流与稀疏化的两阶段解决方案，基于信息论准则与空间准则对记录、属性及数值进行选择性剪枝。实验表明，该系统在TB级数据规模下能实现显著的瓦片尺寸压缩，同时保持视觉保真度与交互性能。