BONSAI introduces a four-layer architecture and four-phase workflow for human-AI co-development of visual analytics applications, shown in case studies to enable efficient novel tool creation and reconstruction from paper descriptions.
D., Endert A., Sanyal J., Chen J
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Visualization researchers propose traceability—recording abundant annotated artifacts, reporting curated research threads, and enabling reading via interfaces—as a way to ensure rigor and transparency in inherently unreproducible design processes.
SuperProvenanceWidgets adds cross-control provenance tracking to visualize how users interact with multiple UI elements over time, demonstrated through usage scenarios and a developer usability assessment.
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BONSAI: A Mixed-Initiative Workspace for Human-AI Co-Development of Visual Analytics Applications
BONSAI introduces a four-layer architecture and four-phase workflow for human-AI co-development of visual analytics applications, shown in case studies to enable efficient novel tool creation and reconstruction from paper descriptions.
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Reflections on Traceability for Visualization Research
Visualization researchers propose traceability—recording abundant annotated artifacts, reporting curated research threads, and enabling reading via interfaces—as a way to ensure rigor and transparency in inherently unreproducible design processes.
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SuperProvenanceWidgets: Tracking and Visualizing Analytic Provenance Across UI Control Elements
SuperProvenanceWidgets adds cross-control provenance tracking to visualize how users interact with multiple UI elements over time, demonstrated through usage scenarios and a developer usability assessment.