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Falcon: Balancing Interactive Latency and Resolution Sensitivity for Scalable Linked Visualizations

Dominik Moritz, Bill Howe, Jeffrey Heer. ACM Human Factors in Computing Systems (CHI), 2019
Figure for Falcon: Balancing Interactive Latency and Resolution Sensitivity for Scalable Linked Visualizations
Falcon visualizing binned aggregates for 180 million flights in a web browser. The brushes select short afternoon flights with no more than a 10 minute arrival delay. The views update instantly when the user draws, moves, or resizes a brush.
Materials
Abstract
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.
BibTeX
@inproceedings{2019-falcon,
  title = {Falcon: Balancing Interactive Latency and Resolution Sensitivity for Scalable Linked Visualizations},
  author = {Moritz, Dominik AND Howe, Bill AND Heer, Jeffrey},
  booktitle = {ACM Human Factors in Computing Systems (CHI)},
  year = {2019},
  url = {https://idl.uw.edu/papers/falcon},
  doi = {10.1145/3290605.3300924}
}