# Semi-Automated Approach for Building Event Logs for Process Mining from Relational Database
## 1 Introduction  
Process mining can discover, monitor, and improve business processes by extracting knowledge from event logs [1, 2]. An event log is a collection (historical data) of execution traces of a business process [3]. Process mining techniques can identify bottlenecks, anticipate problems in process execution, record business rule violations, recommend countermeasures, provide information for decision-making, and streamline the process. In this way, the process discovery task through an algorithm can automatically produce a business process model from the behavior observed in the event log without requiring a-priori information [4, 5]. The conformance checking task verifies the relationship between the behavior observed in the event log and the behavior modeled in the business process, making it possible to identify possible deviations [1, 6]. On the other hand, process enhancement consists of extending or improving the existing process model using the current process information contained in its event log [1, 7], the results obtained from process discovery and conformance checking, and analysis of attribute properties of the event log. When a process-oriented information system is not implemented in the organization, a procedure is required to extract data events from an information system to convert them into an event log [8; 9]. These systems may have many database tables from which event data can be extracted that need to be converted into a structure and format that a process mining method can interpret. Furthermore, the event log generation procedure may involve multiple databases from different back-end systems [10]. A high precision achieved by a process mining method is related to the quality of the event log used [11]. Therefore, validating and verifying event data conversion to event logs is essential to guarantee reliable results. The eXtensible Event Stream (XES) is the standard format for event log specification [12], facilitating the exchange of event data between different systems and implementing process mining techniques. The event log is a fundamental element for implementing methods based on process mining. The building of event logs presents several challenges; for example, extracting events from a relational database, deep knowledge of the domain of the executed business processes is required and of the application context of these business processes. Knowledge of the domain is essential to selecting the correct attributes of the database tables [13]. Hence, experts at the technical and business levels are required since knowing the semantics of the event data values is essential. Several approaches are available in state-of-the-art to support the generation of event logs: in a semi-automatic way from relational databases [14], derive process activities through a method based on natural language inference [15], from a real-world data warehouse of a healthcare system [16], from database redo logs [17; 18], from data stored on the blockchain [19], and conversion of event data to event logs using deep learning [11]. An event data source that has provoked great interest in the process mining community is information systems based on Internet of Things (IoT) technology [20]. These systems generate a large amount of data, which can be collected utilizing robots or independent sensors, and processed automatically by web services, which receive the request, process it, and return a response, allowing an operation to continue in the thing. From a business process point of view, these approaches can be classified within the emerging robotic process automation technology [21]. In these systems, things operate in an interconnected ecosystem, with an autonomous but cooperative operation with other things, embedded systems or software components, to coordinate actions that allow compliance with the objectives and policies previously defined or updated in run-time.