## 2 Literature Review
### Integrated Applications of Process Mining and Simulation Modeling  
Several attempts have been made to create a methodology that combines process mining and simulation. Process mining techniques were implemented to discover the simulation model in a semi-automatic fashion by [PERSON] et al. [4]), who demonstrated that event logs encompass different attributes such as control flow, data, resources, or time and can be used to create a simulation model. [PERSON] [5] investigated the potential of integrating process mining with discrete-event simulation modeling. This paper stated that using historical data and process mining tools, discrete operational processes can be characterized easily. However, the primary challenge lies in interpreting the results from process mining and importing them into simulation tools. The author undertook this challenge in bridging the gap between process mining and discrete-event simulation modeling by creating a compatible tool that can import the process models from the process mining tool into generating a simulation model.  
Process mining has also been applied in conjunction with agent-based simulation [6]. This study illustrates that there is a gap between the high level of abstraction at which the business processes are usually modeled and the low-level characteristics of events that are generated during the execution of the process. This gap was addressed by utilizing process mining techniques to capture this relationship in a hierarchical Markov model. At the same time, agent-based simulation techniques were used to generate low-level behavior for new versions of the process. By inserting changes to the process model, reconfiguring the simulation model, generating a new event log, and mining a new hierarchical model iteratively, this study was able to generate a more accurate and balanced model.  
Process models created manually can depict an idealistic and simpler version of the process by not considering the complex nature of the real-world processes. To address this, analysis of actual event logs using process mining can aid improve the fidelity of the model. To this extent, data mining, and process mining were utilized in conjecture with simulation modeling to present a methodological approach to redesigning the process [7]. This study used process mining to extract the required parameters to create the simulation model based on a realistic model of the process obtained from the event logs. Several redesign alternatives were compared to select the most accurate and reliable model.  
With the increasing availability of fine-grained event data for business processes, one study conducted a comparative experimental evaluation of eleven outcome-oriented predictive monitoring techniques based on real-life event logs [8]. This study used event logs to extract features and trained classification algorithms such as K-nearest neighbor (KNN), random forest (RF), support vector machine (SVM), etc. The result demonstrated the feasibility of using event logs, and classification algorithms to perform outcome-oriented predictive analysis for business processes. However, similar technology can be implemented in other fields such as engineering, healthcare, construction, and navigation, among others.  
Even though there have been developments in integrating process mining and simulation modeling techniques, widespread adoption of these techniques is hindered due to numerous challenges. As summarized in [PERSON] [9], the primary challenges are lack of process knowledge is captured event logs, unstructured nature of data, lack of details, or excessive details in the event log. This study investigates these challenges to create a meaningful, event log with an appropriate amount of abstraction in the data structure.