Extracting and Exploiting the Behavior Business Process Graph through Transition-Centric Event-Log data

In recent years, there has been an intense interest in extracting knowledge from Business Process (BP) execution data provided by Information System (IS). In this area, a set of Process Mining (PM) approaches has been developed. While such conventional PM approaches aim to extract hand-crafted features from the event log, the Deep Learning (DL) models are used to automatically extract the features from the input data. Whereas, the graph representation is the advanced and powerful input format for these DL models. This paper focuses on the pre-processing data representation stage as a starting step for the application of any Machine Learning (ML) technique (process discovery, anomaly detection, classification, recommendation, $\ldots$etc.). This phase aim to represent the BP event-log data transitions as Behavior Graphs (BG). This BG constitutes the backbone of our perspective hierarchical DL framework’s based feature extraction and which allows to learn the unified execution of the process hidden behind the event-log data trace’s.