This paper introduces a novel approach to studying user behavior through the User Behavior Mining Framework, analyzing a unique log of 948,251 complex user interaction traces across 78,547 individuals featuring logs of low-level activities in a complex process environment. Employing methods such as directly-follows-graph analysis and trace clustering and next action prediction, the research uncovers the impact of varying experience levels on user interaction patterns and enhances predictive modeling for action forecasting in complex scenarios. This work not only addresses a significant gap in the field by leveraging an underutilized data source but also highlights the importance of rich, detailed datasets for a comprehensive understanding of user behavior and system interaction.