Discovering Directly-Follows Graph Model for Acyclic Processes
This work addresses a specific issue in process mining for acyclic processes, offering incremental improvements in model accuracy and usability.
The paper tackles the problem of existing process discovery methods generating models with cycles for acyclic processes, and presents a new algorithm that discovers acyclic directly-follows graph models, improving visual clarity and precision.
Process mining is the common name for a range of methods and approaches aimed at analysing and improving processes. Specifically, methods that aim to derive process models from event logs fall under the category of process discovery. Within the range of processes, acyclic processes form a distinct category. In such processes, previously performed actions are not repeated, forming chains of unique actions. However, due to differences in the order of actions, existing process discovery methods can provide models containing cycles even if a process is acyclic. This paper presents a new process discovery algorithm that allows to discover acyclic DFG models for acyclic processes. A model is discovered by partitioning an event log into parts that provide acyclic DFG models and merging them while avoiding the formation of cycles. The resulting algorithm was tested both on real-life and artificial event logs. Absence of cycles improves model visual clarity and precision, also allowing to apply cycle-sensitive methods or visualisations to the model.