Canis: A High‐Level Language for Data‐Driven Chart Animations. (18th July 2020)

• Record Type: Journal Article
• Authors: Ge, T.; Zhao, Y.; Lee, B.; Ren, D.; Chen, B.; Wang, Y.
• Is Part Of: Computer graphics forum. Volume 39:Number 3(2020)
• Page Start: 607
• Page End: 617
• Publication Date: 2020-07-18
• Subjects: CCS Concepts -- Human‐centered computing → Visualization toolkits -- Information visualization -- Visualization systems and tools; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.14005
• Languages: English
• ISSNs: 0167-7055

Abstract

Abstract: In this paper, we introduce Canis, a high‐level domain‐specific language that enables declarative specifications of data‐driven chart animations. By leveraging data‐enriched SVG charts, its grammar of animations can be applied to the charts created by existing chart construction tools. With Canis, designers can select marks from the charts, partition the selected marks into mark units based on data attributes, and apply animation effects to the mark units, with the control of when the effects start. The Canis compiler automatically synthesizes the Lottie animation JSON files [Aira], which can be rendered natively across multiple platforms. To demonstrate Canis' expressiveness, we present a wide range of chart animations. We also evaluate its scalability by showing the effectiveness of our compiler in reducing the output specification size and comparing its performance on different platforms against D3.