Leveraging Multi-Perspective A priori Knowledge in Predictive Business Process Monitoring
Name
Kaur Järvpõld
Abstract
Predictive business process monitoring is an area dedicated to exploiting past process execution data in order to predict the future unfolding of a currently executed business process instance. Most of the research done in this domain focuses on exploiting the past process execution data only, leaving neglected additional a priori knowledge that might become available at runtime. Recently, an approach was proposed, which allows to leverage a priori knowledge on the control flow in the form of LTL-rules. However, cases exist in which more granular a priori knowledge becomes available about perspectives that go be-yond the pure control flow like data, time and resources (multiperspective a priori knowledge).
In this thesis, we propose a technique that enables to leverage multi-perspective a priori knowledge when making predictions of complex sequences, i.e., sequences of events with a subset of the data attributes attached to them. The results, obtained by applying the proposed technique to 20 synthetic logs and 1 real life log, show that the proposed technique is able to overcome state-of-the-art approaches by successfully leveraging multiperspective a priori knowledge.
In this thesis, we propose a technique that enables to leverage multi-perspective a priori knowledge when making predictions of complex sequences, i.e., sequences of events with a subset of the data attributes attached to them. The results, obtained by applying the proposed technique to 20 synthetic logs and 1 real life log, show that the proposed technique is able to overcome state-of-the-art approaches by successfully leveraging multiperspective a priori knowledge.
Graduation Thesis language
English
Graduation Thesis type
Master - Software Engineering
Supervisor(s)
Fabrizio Maria Maggi, Chiara Di Francescomarino, Chiara Ghidini
Defence year
2018