No Grammar to Rule Them All: A Survey of JSON-style DSLs for Visualization
July 16, 2022 ยท The Cartographer ยท ๐ IEEE Transactions on Visualization and Computer Graphics
"No code URL or promise found in abstract"
"Title-pattern auto-detect: No Grammar to Rule Them All: A Survey of JSON-style DSLs for Visualization"
Evidence collected by the PWNC Scanner
Authors
Andrew McNutt
arXiv ID
2207.07998
Category
cs.HC: Human-Computer Interaction
Citations
38
Venue
IEEE Transactions on Visualization and Computer Graphics
Last Checked
2 days ago
Abstract
There has been substantial growth in the use of JSON-based grammars, as well as other standard data serialization languages, to create visualizations. Each of these grammars serves a purpose: some focus on particular computational tasks (such as animation), some are concerned with certain chart types (such as maps), and some target specific data domains (such as ML). Despite the prominence of this interface form, there has been little detailed analysis of the characteristics of these languages. In this study, we survey and analyze the design and implementation of 57 JSON-style DSLs for visualization. We analyze these languages supported by a collected corpus of examples for each DSL (consisting of 4395 instances) across a variety of axes organized into concerns related to domain, conceptual model, language relationships, affordances, and general practicalities. We identify tensions throughout these areas, such as between formal and colloquial specifications, among types of users, and within the composition of languages. Through this work, we seek to support language implementers by elucidating the choices, opportunities, and tradeoffs in visualization DSL design.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Human-Computer Interaction
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
๐ป
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
๐ป
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
๐ป
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
๐ป
Ghosted