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Abstract

The goals of process mining include the improvement, monitoring, optimization, and modeling of business processes. The efficacy of process mining is inherently linked to the soundness of the underlying event data, as the precision and reliability of this input directly influence the resulting outcomes. Extracting event logs from most systems poses a significant challenge for process mining, as events are often not stored natively within the system. This extraction process is time-consuming, complex, and prone to errors, impacting the quality of the event logs.

This thesis explores the design and implementation of an event-oriented system that employs Domain-Driven Design (DDD) and event sourcing. This approach provides an alternative source of data for process mining by natively generating fine-grained events. The implementation focuses on a productivity application designed for use in the healthcare context, demonstrating how event-sourced data can be leveraged for process mining.

The findings indicate that event-sourced data can be effectively used for process mining. How- ever, processing such detailed event data requires careful handling of event sequences, causality, and temporal relationships, adding complexity to the process. Moreover, the integration of DDD facilitates a closer alignment between system design and businesses needs, further enhancing the completeness and correctness of the event logs.

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