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：对可视化分析过程的理解可以从多个方面惠及可视化研究人员，包括改进视觉设计和开发高级交互功能。然而，由于意义建构的复杂性以及我们对相关用户行为认知的不足，用户行为的日志文件仍然难以分析。本研究基于用户行为的综合数据集，结合时间序列分类方法提出了分析方案。我们选择了一个经典的可视化应用——COVID-19数据分析，其常见分析任务涵盖地理空间、时间序列和多属性维度。用户研究收集了用户在桌面与沉浸式可视化两套可比较系统上执行多种可视化任务时的行为数据。我们通过两种尺度下的三种时间序列机器学习算法归纳分类结果，并探究行为特征的影响。研究结果表明，在可视化分析过程中用户行为具有可区分性，且用户的身体行为与所执行的可视化任务之间存在潜在的强关联。我们还通过解读开放式可视化分析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建构提供了新途径。