An interpretable deep fusion framework for event log repair

In executing business processes, issues like information system failures or manual recording errors may lead to data loss in event logs, resulting in missing event logs. Utilizing such missing logs could seriously impact the quality of business process analysis results. To address this scenario, current advanced repair methods rely primarily on deep learning technology to provide intelligent solutions for business processes. However, deep learning technology is often considered a "black-box" model, lacking sufficient interpretability. No method is currently available to provide particular interpretability, especially in repairing specific missing values within the logs. This paper proposes the deep fusion interpretability framework based on artificial intelligence technology to address this issue. In the task of event log repair, this framework gradually transitions from the overall framework's local to global interpretability. It provides local interpretability from the attribute-level data flow perspective, semi-local interpretability from the event-level behavioral control-flow perspective, and global interpretability from the trace-level perspective. Next, we present various modes of multi-head attention within the framework and visualize the process of attention distribution calculation to explain how the framework repairs missing values through the profound combination of multi-head attention mode and context. Finally, Experimental results in real public event logs show that the DFI framework can effectively repair the missing values in event logs and explain the missing value repair process.