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Investigating the Applicability of Cancellation Regions Discovery Algorithm for Synthesis of Process Models from Real Event Logs

Student: Svetlana Zlobina

Supervisor: Irina A. Lomazova

Faculty: Faculty of Computer Science

Educational Programme: System and Software Engineering (Master)

Year of Graduation: 2021

Process mining is a field of data science which aims to improve real business processes by discovering and analyzing models of these processes using event logs. In the real life, many unexpected events may occur during the execution of a process, so that after the certain event several steps of the process are canceled. State-based region algorithm, which is widely used for discovering Petri nets from transition systems, gives models with overcomplicated and poorly readable structure in case of cancellations. Recently a novel method to discover cancellation regions based on transition system decomposition was proposed. In this work we implement the newly suggested method as a ProM and investigate its applicability for process models discovery from real event logs.

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