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Related Experiment Video

Updated: Jul 12, 2025

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

Dupo: A Mixed-Initiative Authoring Tool for Responsive Visualization.

Hyeok Kim, Ryan Rossi, Jessica Hullman

IEEE Transactions on Visualization and Computer Graphics

|October 23, 2023

Summary

This summary is machine-generated.

Dupo, a mixed-initiative system, aids in creating responsive visualizations by combining user control with automated suggestions. This approach helps designers explore and customize designs efficiently for various screen types.

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Area of Science:

Human-Computer Interaction
Information Visualization
Computer Graphics

Background:

Designing responsive visualizations across diverse screen types is complex and time-consuming.
Existing automated methods often lack user agency in design exploration and customization.

Purpose of the Study:

To introduce Dupo, a mixed-initiative system for responsive visualization design.
To enhance user agency and automation in creating adaptable data visualizations.

Main Methods:

Dupo combines a manual interface with a two-step recommender system (Exploration and Alteration).
Users can browse automated design suggestions and customize them for different screen types.
The system was evaluated with six expert responsive visualization authors.

Main Results:

Participants could explore multiple responsive design options without manual prototyping.
Dupo facilitated reasoning about design suggestions, preventing premature fixation on a single design.
Users created satisfactory designs they might have otherwise overlooked.

Conclusions:

Mixed-initiative systems like Dupo can effectively support the creation of responsive visualizations.
Balancing automation with user agency leads to more efficient and satisfactory design outcomes.
Dupo enables rapid prototyping and customization for adaptive data visualization.