Linked visualisations via Galois dependencies

Roly N T Perera, Minh H Nguyen, Tomas Petricek, Meng Wang

Research output: Contribution to journalArticle (Academic Journal)peer-review

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Abstract

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.
Original languageEnglish
Article number7
Pages (from-to)1-29
JournalProceedings of the ACM on Programming Languages
Volume6
Issue numberPOPL
DOIs
Publication statusPublished - 11 Jan 2022

Bibliographical note

Funding Information:
Acknowledgements. Perera and Petricek were supported by The UKRI Strategic Priorities Fund under EPSRC Grant EP/T001569/1, particularly the Tools, Practices and Systems theme within that grant, and by The Alan Turing Institute under EPSRC grant EP/N510129/1. Wang was supported by Expressive High-Level Languages for Bidirectional Transformations, EPSRC Grant EP/T008911/1.

Publisher Copyright:
© 2022 Owner/Author.

Structured keywords

  • Programming Languages

Keywords

  • Galois connections
  • data provenance

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