Self-Driving Process Automation based on Prescriptive Monitoring
Organization
Software Engineering and Information Systems
Abstract
Prescriptive monitoring helps optimize the execution of a process case by providing recommendations to process workers. These recommendations can include, for instance, what activity to execute next. Using different techniques, we use historical data and recommendations. However, optimizing one ongoing case can negatively impact another ongoing case. Thus, it is important to ensure that a recommendation improves the overall process performance. In this thesis, the objective is to use an event log to measure the performance of the process, detect possible recommendations, and assess if the recommendation will improve the overall process performance. To this end, we capitalize on existing methods for performance assessment, prescriptive monitoring, prediction, and simulation methods.
Graduation Theses defence year
2024-2025
Supervisor
Fredrik Milani & Marlon Dumas
Spoken language (s)
English
Requirements for candidates
Level
Masters
Keywords
Application of contact
Name
Fredrik Milani
Phone
E-mail