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.

本研究采集了用户行为的全面数据集，并提出了时间序列分类方法的分析方法。通过对经典的 Covid-19 数据分析应用进行用户研究，我们发现用户行为在可视分析过程中可以区分，并且用户的物理行为与其执行的可视化任务存在潜在的强关联。我们还通过解释可视分析的开放会话来展示我们模型的用法，为研究感知过程提供了一种自动化的方式，避免了繁琐的手动标注。