  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.
