## 1 Introduction  
Nowadays, with the capability of computer hardware, every company can record any events of its operational activities in the system. Recorded events can be used by the company for further analysis. The possible use of recorded event data is to create a data-driven simulation model. The simulation model derived from the event data directly is very useful for users who are unfamiliar with simulations. Discrete-event simulation (DES) is one of the most popular existing simulation types [1]. In DES, an event is typically defined as a specific change in the state of the system at a particular time [1]. Another possible use of the recorded event data is to analyze the discrepancy between the guided plan of operational activity and the real activity in the field. A possible technique to conduct this type of analysis is process mining. Process mining is both data-driven and process-centric [2]. Although process mining is generally used for automated process discovery, it can be used for other purposes such as checking compliance, diagnosing deviations (base and real), highlighting bottlenecks, improving performance, predicting flow times, and recommending action [2]. The input for process mining is an event log [2]. This event log may contain all the events related to operational activity. In process mining, each event generally refers to an activity that occurs at a particular time for specific cases [3]. Evidently, the definition of events in process mining and DES is related. Moreover, event data can be generated as in process mining based on the rules defined in the simulation model. Hence, combining process mining and simulation such that the results of the two can complement each other is reasonable, as suggested by [PERSON] [3]. To the best of our knowledge, the first practical approach that combined process mining and simulation was proposed in 2009 [4]. The author used a process model based on a Petri net representation discovered from an event log using a process discovery algorithm [4]. This Petri net comprises places, transitions, and arcs. The process discovery algorithm used in this study is an alpha algorithm used to mine the workflow process from the event log [5]. The discovered process model represents a control-flow perspective, combined with other analysis techniques such as decision point analysis, organizational miner, and statistical analysis [4]. Decision point analysis is used to mine decisions based on the attribute data that influence the choices made in the process based on past process executions [6]. The decision is triggered when the next execution occurs at a place where more than one possible output (activities) can be obtained. An organizational miner is conducted to mine resource allocations in a specific activity. Several algorithms such as default mining, doing similar task, and agglomerative hierarchical clustering can be used to perform this resource allocation [7]. This aspect is also important in simulation because the execution of an activity in simulation is generally constrained by the resources. Statistical analysis is used to analyze the performance based on the event log. This analysis can also be used to fit the distribution of several parameters for simulation such as the execution time of activity and the arrival time of cases [4]. The results from the organizational miner, decision point analysis, and statistical analysis are then merged and transformed into a colored Petri net (CPN) representation. If a considerable amount of data in the event log required to determine the parameters of the simulation is missing, the missing data can be predicted using machine learning by first changing the representation of the event log into an image [8]. Related with mining the event log, another interesting research in process mining by implementing a verification method for the message passing behavior of IoT systems is proposed to extract event relations from the log and check for any minor deviations that exist in the system [9].