Bachelor Thesis - A recommender system for process discovery

 

Description

Process discovery is an important research field of process mining that deals with the identification of process models to accurately capture the behavior of the system under study. As a prerequisite for many process mining techniques, process discovery algorithms have been widely used in different domains for process optimization. However, the process of selecting the most appropriate discovery technique is often a challenging task for users who lack the technical knowledge and expertise required for process mining. Therefore, the aim of this project is to develop a recommender system for process discovery that will assist users in selecting the most suitable process discovery technique. The recommender system will take into account the characteristics of the input event logs and recommend the most suitable technique based on different measurements such as fitness, precision, etc.

The main objectives of this project are:

• To analyze and compare different process discovery techniques.
• To develop a recommender system for process discovery that takes into account the characteristics of the input event logs and recommends the most suitable technique.
• To evaluate the performance of the recommender system using benchmark datasets.

Expected Results:

• A recommender system for process discovery that will assist users in selecting the most appropriate process discovery technique. The recommender system will take into account the characteristics of the input event logs and recommend the most suitable technique based on user preferences. The evaluation results will demonstrate the effectiveness of the proposed recommender system.

Prerequisites

Good programming skills, specifically Python programming language and web development technologies (Javascript, HTML, CSS, etc.). Knowledge of basic computer science concepts and interests in process mining. Preferably demonstrated by good grades for the respective courses.

Pointers

• W.M.P. van der Aalst. Process Mining: Data Science in Action. Springer-Verlag, Berlin, 2016.
• W.M.P. van der Aalst and J. Carmona, editors. Process Mining Handbook, volume 448 of Lecture Notes in Business Information Processing. Springer-Verlag, Berlin, 2022.
• Ribeiro, Joel, Josep Carmona, Mustafa Mısır, and Michele Sebag. "A recommender system for process discovery." In Business Process Management: 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings 12, pp. 67-83. Springer International Publishing, 2014.

Supervisor

Prof.dr.ir. Wil van der Aalst

Advisor

Tsung-Hao Huang

For more information

Send an e-mail to tsunghao.huang@pads.rwth-aachen.de . Make sure to include detailed information about your background and scores for completed courses.