We present new language-based dynamic analysis techniques for linking visualisations and other structured outputs to data in a fine-grained way, allowing users to explore how data attributes and visual or other output elements are related by selecting (focusing on) substructures of interest. Our approach builds on bidirectional program slicing techiques based on Galois connections, which provide desirable round-tripping properties. Unlike the prior work, our approach allows selections to be negated, equipping the bidirectional analysis with a De Morgan dual which can be used to link different outputs generated from the same input. This offers a principled language-based foundation for a popular view coordination feature called brushing and linking where selections in one chart automatically select corresponding elements in another related chart.
Thu 20 JanDisplayed time zone: Eastern Time (US & Canada) change
15:05 - 16:20
|Isolation without Taxation: Near-Zero-Cost Transitions for WebAssembly and SFIInPerson|
Matthew Kolosick University of California at San Diego, Shravan Ravi Narayan University of California at San Diego, Evan Johnson University of California at San Diego, Conrad Watt University of Cambridge, Michael LeMay Intel Labs, Deepak Garg MPI-SWS, Ranjit Jhala University of California at San Diego, Deian Stefan University of California at San DiegoDOI Media Attached
Yihong Zhang University of Washington, Yisu Remy Wang University of Washington, Max Willsey University of Washington, Zachary Tatlock University of WashingtonDOI Media Attached
|Linked Visualisations via Galois DependenciesRemote|
Roly Perera Alan Turing Institute, Minh Nguyen University of Bristol, Tomas Petricek University of Kent, Meng Wang University of BristolDOI Media Attached