Semi-Automated Approach for Building Event Logs for Process Mining from Relational Database

Abstract

Process mining is a novel alternative that uses event logs to discover, monitor, and improve real business processes through knowledge extraction. Event logs are a prerequisite for any process mining technique. The extraction of event data and event log building is a complex and time-intensive process, with human participation at several stages of the procedure. In this paper, we propose a framework to semi-automatically build an event log based on the XES standard from relational databases. The framework comprises the stages of requirements identification, event log construction, and event log evaluation. In the first stage, the data is interpreted to identify the relationship between the columns and business process activities, then the business process entities are defined. In the second stage, the hierarchical structure of the event log is specified. Likewise, a formal rule set is defined to allow mapping the database columns with the attributes specified in the event log structure, enabling the extraction of attributes. This task is implemented through a correlation method at the case, event, and activity levels, to automatic event log generation. We validate the event log through quality metrics, statistical analysis, and business process discovery. The former allows for determining the quality of the event log built using the metrics of accuracy, completeness, consistency, and uniqueness. The latter evaluates the business process models discovered through precision, coverage, and generalization metrics. The proposed approach was evaluated using the autonomous Internet of Things (IoT) air quality monitoring system’s database and the patient admission and healthcare service delivery database, reaching acceptable values both in the event log quality and in the quality of the business process models discovered.