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MobileVisFixer: Tailoring Web Visualizations for Mobile Phones Leveraging an Explainable Reinforcement Learning
IEEE Transactions on Visualization and Computer Graphics
|October 19, 2020
Summary
MobileVisFixer automates redesigning web visualizations to be mobile-friendly. This reinforcement learning method addresses common issues on small screens, improving user experience.
Area of Science:
- Computer Science
- Human-Computer Interaction
- Data Visualization
Background:
- Mobile devices are the primary web access point, yet many visualizations are not mobile-friendly.
- Manual adaptation of visualizations for different screen sizes is time-consuming and lacks tool support.
Purpose of the Study:
- To introduce MobileVisFixer, an automated method for creating mobile-friendly visualizations.
- To address the challenge of adapting complex data visualizations for small mobile screens.
Main Methods:
- Developed a reinforcement learning framework using a Markov Decision Process (MDP).
- Analyzed web-based SVG visualizations to identify common mobile-friendly issues.
- Employed a greedy heuristic based on Policy Gradient methods for optimization.
Main Results:
- MobileVisFixer successfully addresses four common mobile-friendly issues in Cartesian visualizations.
- The method demonstrates generalizability across various visualizations and is explainable.
- Quantitative evaluations confirm the effectiveness and adaptability of the approach.
Conclusions:
- MobileVisFixer offers an automated solution to enhance visualization accessibility on mobile devices.
- The reinforcement learning framework provides a scalable and effective approach to visualization redesign.
- This work facilitates better user experiences by optimizing visualizations for mobile web access.
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