Machine Learning-Based Framework for Log-Lifting in Business Process Mining Applications

Real-life event logs are typically much less structured and more complex than the predefined business activities they refer to. Most of the existing process mining techniques assume that there is a one-to-one mapping between process model activities and events recorded during process execution. Unfortunately, event logs and process model activities are defined at different levels of granularity. The challenges posed by this discrepancy can be addressed by means of log-lifting. In this work, we develop a machine-learning-based framework aimed at bridging the abstraction level gap between logs and process models. The proposed framework operates in two main phases: log segmentation and machine-learning-based classification. The purpose of the segmentation phase is to identify the potential segment separators in a flow of low-level events, in which each segment corresponds to an unknown high-level activity. For this, we propose a segmentation algorithm based on maximum likelihood with n-gram analysis. In the second phase, event segments are mapped into their corresponding high-level activities using a supervised machine learning technique. Several machine learning classification methods are explored including ANNs, SVMs, and random forest. We demonstrate the applicability of our framework using a real-life event log provided by the SAP company. The results obtained show that a machine learning approach based on the random forest algorithm outperforms the other methods with an accuracy of 96.4%. The testing time was found to be around 0.01s, which makes the algorithm a good candidate for real-time deployment scenarios