Dealing with Complexity in Process Model Discovery Through Segmentation
The fundament of every successful organization is a proper business process management, as it allows maintaining the organization’s production processes and the employed resources in the most sufficient way. Many noticeable problems occurred in production can be analyzed using process mining techniques and event logs obtained from tasks executed during the organization lifetime. One of the common ways to do this is to generate process models in order to study existing operations and explore processes in the organization with the aim to change them. With developing and expanding process mining technique, many methods and tools appeared which can help to solve this kind of task. However, most of the existing tools that are applied to real-life event logs produce spaghetti-like models that are difficult to understand without explanation. In this thesis we try to address this issue by filtering and sorting the logs before mining as well as adjusting model complexity, thus obtaining process models that we will measure and reform to satisfy desired complexity. A final result is a tool that produces a set of simple and understandable process models that the user can select according to his or her choices.
Graduation Thesis language
Graduation Thesis type
Master - Software Engineering
Fabrizio Maria Maggi, Fredrik Payman Milani