ATWL: A Formal Language for Representing, Comparing, and Reusing Visual Analytics Workflows
2026-05-25 • Artificial Intelligence
Artificial IntelligenceHuman-Computer Interaction
AI summaryⓘ
The authors created a new language called ATWL to describe visual analytics workflows in a clear, structured way instead of using messy text. ATWL breaks down workflows into standard parts and actions, making it easier to compare and reuse methods. They used AI help to turn existing research into this new language, building a library of examples. Their tests showed that ATWL helps better organize and scale workflow knowledge compared to regular text descriptions. This approach can make learning and sharing data analysis methods simpler and more precise.
Visual analyticsWorkflow languageDeclarative languageOntologyData transformationFeature engineeringLarge language modelsIterative strategiesAnalytical intentKnowledge representation
Authors
Natalia Andrienko, Gennady Andrienko, Jürgen Bernard, Michael Sedlmair
Abstract
Visual analytics (VA) workflows are inherently complex, involving data transformation, feature engineering, visual representation, and human interpretation. They are typically described in unstructured prose, hindering systematic comparison, reuse of proven strategies, and training of novices. We present Artifact-Transform Workflow Language (ATWL), a domain-agnostic, declarative language that formally represents VA workflows by capturing their structure and underlying analytical intent. ATWL is built upon a modular ontology of eight artifact types (entities, features, arrangements, visualisations, patterns, models, knowledge, specifications) and transforms characterised by standardised intents (e.g., define-unit, characterise, contextualise, abstract). To show that formalisation effort need not impede adoption, we extract workflows from research papers through supervised interaction with LLM agents, reducing the human role to review and refinement. Using this process, we constructed a library of seventeen ATWL workflows from published VA papers. Cross-workflow analysis reveals structural regularities -- a recurrent meta-structure, recurring motifs, reusable building blocks, diverse iterative strategies, and cross-domain equivalences -- that remain invisible in prose. We further evaluate practical utility through a controlled experiment in which the same LLM addressed two analytical problems with the library supplied either as original papers or as ATWL representations. Both forms enabled useful recommendations, but the formal representation systematically added explicit iteration structure, typed data flow, fragment-level adaptation provenance, and compactness supporting scaling beyond what prose libraries can fit in an LLM's context. ATWL enables a transition from narrative descriptions to formally represented, comparable, and reusable analytical knowledge.