Atlas: Grammar-based Procedural Generation of Data Visualizations

Liu, Zhicheng; Chen, Chen; Morales, Francisco; Zhao, Yanjun

doi:10.1109/vis49827.2021.9623315

Cited by 8 publications

(13 citation statements)

References 17 publications

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“…We believe that some of the usability issues found in JSON-style visualization DSLs [65] can be addressed through careful enhancements to end-user tooling. Merino et al explore this in their system for creating notebooks tuned to individual DSLs [75].…”

Section: Discussionmentioning

confidence: 99%

“…However, some DSLs use imperative syntax (e.g. shaders or Atlas [65]), but all DSLs in our study are declarative and the closest exceptions are pipeline models.…”

Section: Domain Specific Languagesmentioning

confidence: 99%

“…Despite their popularity, JSON-based languages are no panacea. They are sometimes maligned for their lack of programming usability features [53], rendering them hard to learn, debug, and extend [65]. These criticisms can be extended through the Cognitive Dimensions of Notations (CDN) [4], which are a suite of lightweight heuristics (highlighted throughout) that characterize the usability of notational interfaces.…”

Section: Domain Specific Languagesmentioning

confidence: 99%

“…To do so, we analyzed each surveyed language across a set of topics. Our initial selection of topics was motivated by discussions of DSLs in general [24,65,76,108], visualization DSLs [65,117], as well as related work on visualization authoring systems [94]. We iteratively added and removed axes of analysis (analogous to codes) until a theoretical saturation was reached.…”

Section: Practicalitiesmentioning

confidence: 99%

“…While it is sometimes derided for usability issues [24,53,65], this language style has a variety of benefits. DSLs which employ it can be expressive, allowing for the concise manipulation of complex specifications with minimal textual modification [97].…”

Section: Introductionmentioning

confidence: 99%

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No Grammar to Rule Them All: A Survey of JSON-style DSLs for Visualization

McNutt¹

2022

Preprint

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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.

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Section: Discussionmentioning

confidence: 99%

“…However, some DSLs use imperative syntax (e.g. shaders or Atlas [65]), but all DSLs in our study are declarative and the closest exceptions are pipeline models.…”

Section: Domain Specific Languagesmentioning

confidence: 99%

Section: Domain Specific Languagesmentioning

confidence: 99%

Section: Practicalitiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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No Grammar to Rule Them All: A Survey of JSON-style DSLs for Visualization

McNutt¹

2022

Preprint

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No Grammar to Rule Them All: A Survey of JSON-style DSLs for Visualization

McNutt

2022

IEEE Trans. Visual. Comput. Graphics

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Augmenting text-based programming with rich structured interactions has been explored in many ways. Among these, projectional editors offer an enticing combination of structure editing and domain-specific program visualization. Yet such tools are typically bespoke and expensive to produce, leaving them inaccessible to many DSL and application designers.We describe a relatively inexpensive way to build rich projectional editors for a large class of DSLs-namely, those defined using JSON. Given any such JSON-based DSL, we derive a projectional editor through (i) a language-agnostic mapping from JSON Schemas to structure-editor GUIs and (ii) an API for application designers to implement custom views for the domainspecific types described in a schema. We implement these ideas in a prototype, Prong, which we illustrate with several examples including the Vega and Vega-Lite data visualization DSLs.

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Data Formulator: AI-powered Concept-driven Visualization Authoring

Wang,

Thompson,

Lee

2023

IEEE Trans. Visual. Comput. Graphics

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With most modern visualization tools, authors need to transform their data into tidy formats to create visualizations they want. Because this requires experience with programming or separate data processing tools, data transformation remains a barrier in visualization authoring. To address this challenge, we present a new visualization paradigm, concept binding, that separates high-level visualization intents and low-level data transformation steps, leveraging an AI agent. We realize this paradigm in Data Formulator, an interactive visualization authoring tool. With Data Formulator, authors first define data concepts they plan to visualize using natural languages or examples, and then bind them to visual channels. Data Formulator then dispatches its AI-agent to automatically transform the input data to surface these concepts and generate desired visualizations. When presenting the results (transformed table and output visualizations) from the AI agent, Data Formulator provides feedback to help authors inspect and understand them. A user study with 10 participants shows that participants could learn and use Data Formulator to create visualizations that involve challenging data transformations, and presents interesting future research directions.

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Atlas: Grammar-based Procedural Generation of Data Visualizations

Cited by 8 publications

References 17 publications

No Grammar to Rule Them All: A Survey of JSON-style DSLs for Visualization

No Grammar to Rule Them All: A Survey of JSON-style DSLs for Visualization

No Grammar to Rule Them All: A Survey of JSON-style DSLs for Visualization

Data Formulator: AI-powered Concept-driven Visualization Authoring

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