# Behavioral and Performance Analysis of a Real-Time Case Study Event Log: A Process Mining Approach
## 1 Introduction  
Traditionally, organizations are divided into departments such as production, sales, etc. These departments have their data collection and analysis systems. However, other departments need to know what work is being done, leading to confusion. Modern information systems have emerged to overcome these problems. Most companies and organizations store their data in information systems (IS), such as executive information systems and enterprise resource planning (ERP) systems. ERP systems are growing inpopularity because they view the entire organization as one system and its departments as sub-systems [1]. These systems overcome the traditional problems as all the organization's information is stored centrally and available to every department, bringing many benefits to the organization, such as process integration, data transparency, automation to increase productivity, etc. ERP is a business process management software that helps companies integrate internal and external management information, such as finances, procurement, and customer relationship management. Many systems store all relevant events in a structured form, usually as logs, also known as audit trails or event logs [2; 3]. Over the past few decades, auditors have faced many problems, such as the inability to detect and prevent accounting errors because the systems or tool kits provided to auditors cannot detect all errors and fraud. Many system owners also need more information about what is happening because data mining tools are used in many areas to support business decisions, but they are bad for the process. Organizations spend a lot of money on process modeling. Manual modeling of processes means models need to be updated. Process mining makes sense at this point because it automatically creates process models from log data that can be updated at any time [4]. Process mining aims to extract knowledge from event logs using various tools, strategies, and methods to identify, monitor, and improve real-world processes. Throughput, bottlenecks, and variance are just a few examples of process performance metrics that can be used to analyze processes with process mining [5]. Process mining technology is very suitable for extracting information about existing processes from ERP systems. When a real-time process is executed in an ERP system, the generated data are used to reform the process model [6; 7]. Discovery is a major application domain of process mining, which aims to discover process models by analyzing event logs and extracting knowledge from them. This is the only prior information that is available at this stage to analyze event logs through multiple algorithms, automatically generate Petri net models, and accurately capture the actual control flow of business operations. Conformance is a specific type of analysis that verifies the accuracy of a discovered or ideal model by detecting deviations (who, what, when, and where) based on a comparison between the discovered model and the event log. It also detects the strength level of the model (how close it is to the ideal business process or model). Conformance checks reiterate the notion that logs are reflected in the process model and examine bottlenecks and time stamps associated with each event and process. Process mining also covers three different perspectives. These are used to answer questions such as 'how', 'what', and 'who'. The sequence of activities or the flow of a process is what the term 'process perspective' means. The organizational perspective is used to answer the question of 'who is running the processes and how are they related'. The goal of this perspective is to structure the organization through the visibility of relationships between performers or between performers and tasks. The characteristics of the case are the focus of the case perspective. It helps to clarify 'what happened to this particular operation? [8; 9].