UT Institute of Computer Science Graduation Theses Registry

A Tool for Prescriptive Monitoring of Business Processes
Name Aladdin Shikhizada
Abstract Most companies are deeply concerned about monitoring their business processes. It has special importance for them because in this way they can detect and avoid undesired outcome that can happen. In this context, prescriptive process monitoring techniques aim at analyzing historical behavior of business processes recorded in event logs using machine learning algorithms and then use this information for providing recommendations about actions to take in ongoing process executions to achieve a desired outcome.
In this research, our goal is to build a process-oriented recommender system by implementing two approaches for prescriptive process monitoring. The system takes an event log as input, builds a predictor based on the information retrieved from the log and use it for providing recommendations on validation data.
Graduation Thesis language English
Graduation Thesis type Master - Software Engineering
Supervisor(s) Fabrizio Maria Maggi
Defence year 2020