Bachelor Thesis - Localized Conformance and Performance Analysis Based on Event Data: Diagnosing Individual Places

Contact

Prof. Dr. Wil van der Aalst

Name

Wil van der Aalst

Chairholder

Phone

work
+49 241 80 21900

Email

E-Mail
 

Description

Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. Process mining seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). Various process modeling notations are available and many can be expressed in terms of Petri nets (after some abstraction). Hence a process model can be viewed as a set of places. Most approach try to analyze the performance of the process as a whole. The idea of this bachelor project would be to take a process model and analyze individual places using event data. Conformance and performance can be analyzed very locally and reveal trends, etc. The goal is to develop a ProM plug-in taking a process model and event logs as input. Real-life data sets will be provided to test the approach.

Prerequisites

Good programming skills and knowledge of basic computer science concepts

Pointers

Supervisor

Prof. Wil van der Aalst

Advisor

TBD

For more Information

Send an e-mail to . Make sure to include some information about your background and scores for completed courses.