Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2020.1734716
Automated simulation and verification of process models discovered by process mining
Ivona Zakarija
; Department of Electrical Engineering and Computing, University of Dubrovnik, Dubrovnik, Croatia
Frano Škopljanac-Mačina
; Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
Bruno Blašković
; Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
Sažetak
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.
Ključne riječi
Process mining; IoT; model checking; inductive machine learning; Big Data; MAS
Hrčak ID:
239873
URI
Datum izdavanja:
16.3.2020.
Posjeta: 1.647 *