Master Thesis - Sampling Techniques for Lower Bound Approximation in Conformance Checking

Kontakt

Mohammadreza Fani Sani

Name

Mohammadreza Fani Sani

Wissenschaftlicher Mitarbeiter

Telefon

work
+49 241 80 21908

E-Mail

E-Mail

Kontakt

Sebastiaan J. van Zelst

Name

Sebastiaan J. van Zelst

Wissenschaftlicher Mitarbeiter - Fraunhofer FIT

Telefon

work
+49 241 80 21911

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 conformance checking, in which one aims to detect inconstancies between the recorded event data and a corresponding reference process model. Using the proposed algorithms in this field, we help business owners to detect deviations and frauds in their business.

Many of the state-of-the-art conformance checking algorithms provide highly accurate results, however, at the same time, they are rather complex and thus very time consuming. 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 an exact conformance checking result in order to answer relevant business questions. For example, a manager might simply be interested whether or not his data conforms for at least 75% with the given reference model.

Therefore, in this Master project, we plan to investigate techniques that allow us to obtain (approximate) conformance checking lower bounds. An initial strategy to reach this goal is by simply sampling event logs in a smart way, which already reduce the size of the input event data, and the corresponding algorithmic complexity. The output of this thesis should help users to have (approximate) conformance checking lower bound values in a limited time.

Prerequisites

Good programming skills and knowledge of process mining specifically conformance checking. 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 and . Make sure to include a C.V., detailed information about your background and scores for completed courses.