Falcon: Balancing Interactive Latency and Resolution Sensitivity for Scalable Linked Visualizations
Falcon: Balancing Interactive Latency and Resolution Sensitivity for Scalable Linked Visualizations
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DOI：
10.1145/3290605.3300924
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发表时间：
2019-01
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通讯作者：
Dominik Moritz;Bill Howe;Jeffrey Heer
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作者：
Dominik Moritz;Bill Howe;Jeffrey Heer
我们提供以用户为中心的预取和索引方法，这些方法可在链接的可视化之间提供低延迟交互，从而实现对十亿条记录数据集的冷启动探索。我们在 Falcon 中实现我们的方法，Falcon 是一个基于网络的系统，它在延迟和分辨率之间进行原则性的权衡，以优化刷机和视图切换时间。为了优化对延迟敏感的刷动操作，Falcon 在用户刷入的活动视图发生更改时重新索引数据。为了限制视图切换时间，Falcon 首先加载降低的交互分辨率，然后逐步改进它们。基准测试显示，Falcon 可以维持 50fps 的实时交互性，以实现像素级刷动和跨多个可视化的链接，而无需昂贵的预计算。无论数据集的数据大小如何，从浏览器中的数百万条记录到连接到后备数据库系统时的数十亿条记录，我们都显示出恒定的刷牙性能。
We contribute user-centered prefetching and indexing methods that provide low-latency interactions across linked visualizations, enabling cold-start exploration of billion-record datasets. We implement our methods in Falcon, a web-based system that makes principled trade-offs between latency and resolution to optimize brushing and view switching times. To optimize latency-sensitive brushing actions, Falcon reindexes data upon changes to the active view a user is brushing in. To limit view switching times, Falcon initially loads reduced interactive resolutions, then progressively improves them. Benchmarks show that Falcon sustains real-time interactivity of 50fps for pixel-level brushing and linking across multiple visualizations with no costly precomputation. We show constant brushing performance regardless of data size on datasets ranging from millions of records in the browser to billions when connected to a backing database system.