Master Thesis - Highway Process Model Discovery

Kontakt

Mohammadreza Fani Sani

Name

Mohammadreza Fani Sani

Wissenschaftlicher Mitarbeiter

Telefon

work
+49 241 80 21908

E-Mail

E-Mail

Kontakt

Dr. ir. Sebastiaan J. van Zelst

Name

Sebastiaan J. van Zelst

Wissenschaftlicher Mitarbeiter - Fraunhofer FIT

Telefon

work
+49 241 80 21926

E-Mail

E-Mail
 

Description

Process Mining is a research discipline that is positioned at the intersection of data-driven methods like machine learning and data mining on the one hand and Business Process Modeling (BPM) on the other hand. It aims to discover, monitor, and enhance processes by extracting knowledge from event data that can be extracted from almost all modern databases.

In process mining, event data, originating from the execution of a (business) process, stored in the underlying information systems of a company is often used as a basis. One of the sub-domains of process mining is process discovery, in which one aims to analyze the recorded event data and find the corresponding process model. Using the proposed algorithms in this field, we help business owners to understand better what is going on in their business.

Many of the state-of-the-art process discovery algorithms provide highly complex and incomprehensible results. This problem is one of the reasons that commercial tools did not yet incorporate and/or use most of these algorithms. At the same time, in a lot of process mining analysis scenario’s, one does not need all details of infrequent activities and their relation. Instead, it is more interesting to know, what is the skeleton of the process and how different components interact with each others. If we consider process models as navigation maps, showing all detail information, when we are on a highway just increases the complexity and consequently decreases the uncerstandability.

Therefore, in this Master project, we plan to investigate techniques that allow us to obtain a more general process model. An initial strategy to reach this goal is by merging activities and remove some less frequent behavior in event logs. The output of this thesis should help users to have hierarchical process models. Moreover, we expected that by modifying the event logs using the proposed method in this thesis, we increase the overall quality of discovered process models.

Prerequisites

Good programming skills and knowledge of process mining specifically process discovery. Experience with working ProM is a benefit.

Pointers

Supervisor

prof.dr.ir. Wil van der Aalst

Advisors

Mohammadreza Fani Sani (primary advisor) and Sebastiaan van Zelst (secondary advisor)

For more information

Send an e-mail to . Make sure to include a C.V., detailed information about your background and scores for completed courses.