2022/03/15 by Hyeok Kim, Ryan A. Rossi, Ryan Rossi +5 · 24 citations
Computer Science · Social Sciences · #Artificial intelligence #Cicero #Compiler #Computer science #Data Visualization and Analytics #Grammar #Human–computer interaction #Multimedia Communication and Technology #Parsing #Programming language #Set (abstract data type) #Syntax #Video Analysis and Summarization #Visualization #cs.HC
paper · pdf · doi:10.1145/3491102.3517455
published in CHI Conference on Human Factors in Computing Systems, 1-15 · 14 pages, 15 figures, accepted to CHI 2022
arxiv created 2022/03/15 · arxiv updated 2022/03/17 · openalex publication_date 2022/04/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Designing responsive visualizations can be cast as applying transformations to a source view to render it suitable for a different screen size. However, designing responsive visualizations is often tedious as authors must manually apply and reason about candidate transformations. We present Cicero, a declarative grammar for concisely specifying responsive visualization transformations which paves the way for more intelligent responsive visualization authoring tools. Cicero's flexible specifier syntax allows authors to select visualization elements to transform, independent of the source view's structure. Cicero encodes a concise set of actions to encode a diverse set of transformations in both desktop-first and mobile-first design processes. Authors can ultimately reuse design-agnostic transformations across different visualizations. To demonstrate the utility of Cicero, we develop a compiler to an extended version of Vega-Lite, and provide principles for our compiler. We further discuss the incorporation of Cicero into responsive visualization authoring tools, such as a design recommender.