一句话：提出基于 Galois 连接的双向程序切片动态分析，支持可否定选择，为 brushing and linking 提供语言基础。
论文提出一类新的基于语言的动态分析技术，用于把可视化及其他结构化输出与数据细粒度地关联起来，使用户能通过选择感兴趣的子结构来探索数据属性与输出元素之间的关系。方法建立在基于 Galois 连接的双向程序切片之上，从而获得良好的往返（round-tripping）性质。与已有工作不同，该工作允许选择被否定，为双向分析引入 De Morgan 对偶，因此可以关联由同一输入生成的不同输出。这为常见的视图协调功能 brushing and linking 提供了有原则的语言基础：在一个图表中的选择可自动选中另一相关图表中的对应元素。
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.
@article{paperbot1561,
title = {Linked visualisations via Galois dependencies},
author = {Roly Perera and Minh Nguyen and Tomas Petricek and Meng Wang},
journal = {Proceedings of the ACM on Programming Languages},
volume = {6},
number = {POPL},
year = {2022},
doi = {10.1145/3498668}
}