Automated simulation and verification of process models discovered by process mining
This work addresses the challenge of verifying and simulating process models for business processes, particularly in multi-agent systems like hotel management, but it appears incremental as it applies existing methods to a new domain.
The paper tackles the problem of analyzing process models discovered from event data in a hotel's Property Management System, using an inductive machine learning method to build models and automated verification with Spin model checker and LTL specifications, resulting in analysis suitable for process model repair.
This paper presents a novel approach for automated analysis of process models discovered using process mining techniques. Process mining explores underlying processes hidden in the event data generated by various devices. Our proposed Inductive machine learning method was used to build business process models based on actual event log data obtained from a hotel's Property Management System (PMS). The PMS can be considered as a Multi Agent System (MAS) because it is integrated with a variety of external systems and IoT devices. Collected event log combines data on guests stay recorded by hotel staff, as well as data streams captured from telephone exchange and other external IoT devices. Next, we performed automated analysis of the discovered process models using formal methods. Spin model checker was used to simulate process model executions and automatically verify the process model. We proposed an algorithm for the automatic transformation of the discovered process model into a verification model. Additionally, we developed a generator of positive and negative examples. In the verification stage, we have also used Linear temporal logic (LTL) to define requested system specifications. We find that the analysis results will be well suited for process model repair.