马赛克选择:管理和优化可扩展数据可视化系统的用户选择
IEEE transactions on visualization and computer graphics
|November 20, 2025
概括
马赛克选择优化大型数据集的交互可视化. 该模型能够在多个可视化中快速,低延迟的数据过和更新,改善数百万条记录的性能.
科学领域:
- 计算机科学 计算机科学
- 数据可视化 数据可视化
- 人与计算机的交互
背景情况:
- 交互式可视化与大量数据集 (数百万+记录) 的实时分析作斗争.
- 用户对过数据的选择可能很复杂,需要低延迟的更新.
研究的目的:
- 介绍Mosaic Selections,这是一个用于管理和优化交互式可视化中用户选择的新型模型.
- 为了实现高效,实时与大数据集的交互.
主要方法:
- 开发了Mosaic Selections,这是一个将过器预言集成到可视化和输入小部件的数据查询中的模型.
- 实现自动优化,包括预先汇总数据,基于查询和选择预言分析.
- 在开源的Mosaic架构中正式确定了选择模型和优化技术.
主要成果:
- 与未优化查询和现有的Vega优化器相比,实现了基于选择的优化延迟的数量级改进.
- 证明了对复杂,多组件用户选择的高效处理.
- 验证了数百万和数十亿条记录的可扩展性.
结论:
- 马赛克选择提供了一个灵活和可互操作的框架,用于在可视化中过数据.
- 该模型的自动优化显著提高了使用大型数据集交互式可视化的性能.
- 实现实时交互和分析大规模数据规模.
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