Prediction of Next Events in Business Processes: A Deep Learning Approach

Abstract

Business Process Mining is considered one of the merging fields that focusses on analyzing Business Process Models (BPM), by extracting knowledge from event logs generated by various information systems, for the sake of auditing, monitoring, and analysis of business activities for future improvement and optimization throughout the entire lifecycle of such processes, from creation to conclusion. In this work, Long Short-Term Memory (LSTM) Neural Network was utilized for the prediction of the execution of cases, through training and testing the model on event traces extracted from event logs related to a given business process model. From the initial results we obtained, our model was able to predict the next activity in the sequence with high accuracy. The approach consisted of three phases: preprocessing the logs, classification, and categorization and all the activities related to implementing the LSTM model, including network design, training, and model selection. The predictive analysis achieved in this work can be extended to include anomaly detection capabilities, to detect any anomalous events or activities captured in the event logs.