  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.
