An Automated Approach to Reasoning About Task-Oriented Insights in Responsive Visualization
An Automated Approach to Reasoning About Task-Oriented Insights in Responsive Visualization
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DOI：
10.1109/tvcg.2021.3114782
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发表时间：
2021-07
影响因子：
5.2
通讯作者：
Hyeok Kim;Ryan A. Rossi;Abhraneel Sarma;Dominik Moritz;J. Hullman
中科院分区：
文献类型：
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作者：
Hyeok Kim;Ryan A. Rossi;Abhraneel Sarma;Dominik Moritz;J. Hullman
作者在为桌面和移动设备创建可视化（即响应式可视化）时，经常通过重新缩放、聚合和其他技术将大屏幕可视化转换为较小的显示器。然而，转换可能会改变大屏幕视图所暗示的关系或模式，这要求作者在调整较小显示器的设计时仔细推理要保留哪些信息。我们提出了一种自动化方法来近似在源可视化的响应式转换中失去对面向任务的可视化见解（识别、比较和趋势）的支持。我们将识别、比较和趋势损失操作为目标函数，通过将渲染的源可视化的属性与每个实现的目标（小屏幕）可视化进行比较来计算。为了评估我们方法的实用性，我们在一组源可视化中对人类排名的小屏幕替代可视化训练机器学习模型。我们发现我们的方法在排名可视化方面达到了 84% 的准确率（随机森林模型）。我们在原型响应式可视化推荐器中演示了这种方法，该推荐器使用答案集编程枚举响应式转换，并使用我们的损失度量来评估面向任务的见解的保留。我们讨论了我们的方法对开发自动化和半自动化响应式可视化推荐的影响。
Authors often transform a large screen visualization for smaller displays through rescaling, aggregation and other techniques when creating visualizations for both desktop and mobile devices (i.e., responsive visualization). However, transformations can alter relationships or patterns implied by the large screen view, requiring authors to reason carefully about what information to preserve while adjusting their design for the smaller display. We propose an automated approach to approximating the loss of support for task-oriented visualization insights (identification, comparison, and trend) in responsive transformation of a source visualization. We operationalize identification, comparison, and trend loss as objective functions calculated by comparing properties of the rendered source visualization to each realized target (small screen) visualization. To evaluate the utility of our approach, we train machine learning models on human ranked small screen alternative visualizations across a set of source visualizations. We find that our approach achieves an accuracy of 84% (random forest model) in ranking visualizations. We demonstrate this approach in a prototype responsive visualization recommender that enumerates responsive transformations using Answer Set Programming and evaluates the preservation of task-oriented insights using our loss measures. We discuss implications of our approach for the development of automated and semi-automated responsive visualization recommendation.