On 23 March 2020 at 10.15 a.m., in Narva Rd. 18 room 1020, Adriano Augusto will defend his thesis Accurate and Efficient Discovery of Process Models from Event Logs for obtaining the degree of Doctor of Philosophy (Computer Science).
Prof. Marlon Dumas (Institute of Computer Science UT);
Prof. Marcello La Rosa (University of Melbourne, Australia).
Prof. Benoit Depaire (Hasselt University, Belgium);
Prof. Remco Dijkman (Eindhoven University of Technology, The Netherlands).
Everyday, companies’ employees perform activities with the goal of providing services (or products) to their customers. A sequence of such activities is known as business process. The quality and the efficiency of a business process directly influence the customer experience. In a competitive business environment, achieving a great customer experience is fundamental to be a successful company. For this reason, companies are interested in identifying their business processes to analyse and improve them.
To analyse and improve a business process, it is generally useful to first write it down in the form of a graphical representation, namely a business process model. Drawing such process models manually is time-consuming because of the time it takes to collect detailed information about the execution of the process. Also, manually drawn process models are often incomplete because it is difficult to uncover every possible execution path in the process via manual data collection.
Automated process discovery allows business analysts to exploit process' execution data to automatically discover process models. Discovering high-quality process models is extremely
important to reduce the time spent enhancing them and to avoid mistakes during process analysis. The quality of an automatically discovered process model depends on both the input data and the automated process discovery application that is used.
In this thesis, we provide an overview of the available algorithms to perform automated process discovery. We identify deficiencies in existing algorithms, and we propose a new algorithm,
called Split Miner, which is faster and consistently discovers more accurate process models than existing algorithms. We also propose a new approach to measure the accuracy of automatically discovered process models in a fine-grained manner, and we use this new measurement approach to optimize the accuracy of automatically discovered process models