MobileVisFixer: Tailoring Web Visualizations for Mobile Phones Leveraging an Explainable Reinforcement Learning Framework

Aoyu Wu; Wai Tong; Tim DwyerBongshin LeePetra IsenbergHuamin Qu

首页> 外文期刊>IEEE transactions on visualization and computer graphics >MobileVisFixer: Tailoring Web Visualizations for Mobile Phones Leveraging an Explainable Reinforcement Learning Framework

【24h】

MobileVisFixer: Tailoring Web Visualizations for Mobile Phones Leveraging an Explainable Reinforcement Learning Framework

机译：MobileVisFixer: Tailoring Web Visualizations for Mobile Phones Leveraging an Explainable Reinforcement Learning Framework

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相关主题

摘要

We contribute MobileVisFixer, a new method to make visualizations more mobile-friendly. Although mobile devices have become the primary means of accessing information on the web, many existing visualizations are not optimized for small screens and can lead to a frustrating user experience. Currently, practitioners and researchers have to engage in a tedious and time-consuming process to ensure that their designs scale to screens of different sizes, and existing toolkits and libraries provide little support in diagnosing and repairing issues. To address this challenge, MobileVisFixer automates a mobile-friendly visualization re-design process with a novel reinforcement learning framework. To inform the design of MobileVisFixer, we first collected and analyzed SVG-based visualizations on the web, and identified five common mobile-friendly issues. MobileVisFixer addresses four of these issues on single-view Cartesian visualizations with linear or discrete scales by a Markov Decision Process model that is both generalizable across various visualizations and fully explainable. MobileVisFixer deconstructs charts into declarative formats, and uses a greedy heuristic based on Policy Gradient methods to find solutions to this difficult, multi-criteria optimization problem in reasonable time. In addition, MobileVisFixer can be easily extended with the incorporation of optimization algorithms for data visualizations. Quantitative evaluation on two real-world datasets demonstrates the effectiveness and generalizability of our method.

著录项

来源
《IEEE transactions on visualization and computer graphics》 |2021年第2期|464-474|共11页
作者
Aoyu Wu; Wai Tong; Tim DwyerBongshin LeePetra IsenbergHuamin Qu;
展开▼
作者单位

Hong Kong University of Science and Technology;

Monash University;

Microsoft ResearchInria;

展开▼
收录信息
原文格式 PDF
正文语种英语
中图分类模式识别与装置;
关键词
Data visualization; Visualization; Mobile handsets; Sociology; Statistics; Encoding; Layout; Mobile visualization; Responsive visualization; Machine learning for visualizations; Reinforcement learning;

MobileVisFixer: Tailoring Web Visualizations for Mobile Phones Leveraging an Explainable Reinforcement Learning Framework

摘要

著录项

相关主题

期刊订阅