The understanding of visual analytics process can benefit visualization researchers from multiple aspects, including improving visual designs and developing advanced interaction functions. However, the log files of user behaviors are still hard to analyze due to the complexity of sensemaking and our lack of knowledge on the related user behaviors. This work presents a study on a comprehensive data collection of user behaviors, and our analysis approach with time-series classification methods. We have chosen a classical visualization application, Covid-19 data analysis, with common analysis tasks covering geo-spatial, time-series and multi-attributes. Our user study collects user behaviors on a diverse set of visualization tasks with two comparable systems, desktop and immersive visualizations. We summarize the classification results with three time-series machine learning algorithms at two scales, and explore the influences of behavior features. Our results reveal that user behaviors can be distinguished during the process of visual analytics and there is a potentially strong association between the physical behaviors of users and the visualization tasks they perform. We also demonstrate the usage of our models by interpreting open sessions of visual analytics, which provides an automatic way to study sensemaking without tedious manual annotations.

翻译：对可视分析过程的理解能够从多个方面惠及可视化研究人员，包括改进视觉设计以及开发高级交互功能。然而，由于意义构建的复杂性以及我们对相关用户行为知识的匮乏，用户行为的日志文件仍然难以分析。本文针对用户行为的全面数据收集展开研究，并提出结合时间序列分类方法的分析思路。我们选取了经典的可视化应用场景——新冠疫情数据分析，其常见分析任务涵盖地理空间、时间序列及多属性维度。通过用户研究，我们在桌面端与沉浸式可视化两套可比系统中，收集了用户完成多样化可视化任务时的行为数据。我们采用三种时间序列机器学习算法，在两个尺度上对分类结果进行归纳，并探究了行为特征的影响。研究结果表明，用户在可视分析过程中的行为具有可区分性，且用户的身体行为与其执行的可视化任务之间存在潜在的强关联。我们还通过解释可视分析的开放会话展示了模型的应用价值，这为无需繁琐人工标注即可自动研究意义构建提供了有效途径。