Predictive Monitoring of Multi-level Processes
Name
Henri Trees
Abstract
The ever increasing use of Information Systems causes ever more information to be stored. As organizations and businesses become more efficient due to competition they need to gain competitive advantage over others. More and more companies and institutions have turned to Information Technology to find business value in a data-driven world. Modern Information Systems maintain records of process events, which correspond to real-life activities. As processes evolve and become more complex, so does the information that reflects them. In this thesis, we propose an approach to predictive monitoring of complex multi-level processes. In this context, a multi-level process consists of a high-level parent process which spawns multiple low-level subprocesses, which have their own life cycle and run independently of one another. The author proposes constructs called milestones, which include both parent- and subprocesses and are used for the predictive monitoring classification task. This approach has been validated on a real-life event log of the business-to-business change management process in place at Baltic's largest telecommunications company Telia Estonia.
Graduation Thesis language
English
Graduation Thesis type
Master - Software Engineering
Supervisor(s)
Anna Leontjeva; Marlon Dumas
Defence year
2016