Open Topics

The Workflow Systems and Technologies Group offers topics for bachelor and master theses as well as Master Praktikum. The following list contains a number of current suggestions for topics.

To discuss one of those or any other topic in this research field, please contact the particulary named supervisor.

» Jump to Bachelor thesis topics

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This list will be updated continuously!

Master thesis topics


At the moment there are no open Master thesis topics.

Bachelor theses topics

Supervision Univ.-Prof. Dr. Erich Schikuta

Topic 1: Implementation of eLearning Components in DAPlayground

DAPlayground is an interactive eLearning system visualizing the dynamic behaviour of algorithms and data structures. The goal of DAPlayground is to be used as supporting tool in the course 051024 VU Algorithmen und Datenstrukturen 1. For information on DAPlayground see its website

Goal: Extension of DAPlaygroud by new modules, which are

- Linear Hashing

- Extendible Hashing

- Coalesced Hashing


- Merge Sort

- Linked List

- Stack and Queue


Number of Students: 1 student per module

Recommended Requirements: Javascript, Node.js, vue.js etc.

Supervisor: Erich Schikuta,

Topic 2: Quantifying pressing behaviour in football from positional data

Goal: Implement an algorithm that extracts information about the defensive organization of a football team from large positional data sets (XY trajectories of the players and the ball) for match analysis purposes. In particular, find and implement an algorithm to compute which matchups between attackers and defenders exist and which players and zones tend to be left open.

Recommended Requirements: Python, basic football understanding.

Supervisors: Erich Schikuta <> and Jonas Bischofberger <>

Recommended Literature:

Forcher, L., Altmann, S., Forcher, L., Jekauc, D., & Kempe, M. (2022). The use of player tracking data to analyze defensive play in professional soccer - A scoping review. International Journal of Sports Science & Coaching, 17(6), 1567–1592.

Forcher, L., Beckmann, T., Wohak, O., Romeike, C., Graf, F., & Altmann, S. (2023). Prediction of defensive success in elite soccer using machine learning-Tactical analysis of defensive play using tracking data and explainable AI. Science and Medicine in Football, (just-accepted).

Supervision Marian Lux

Topic: Process Mining visualization tool in Python


Improving/maintaining and providing new features to the already existing process mining tool from a previous bachelor thesis [1]

Different topics based on this process mining tool are possible. Some of them are listed below and the scope of the work is agreed with the supervisor at the beginning. Please write an email with your preferences to the supervisor:

  • Bug fixing for handling a huge amount of input data [1]
  • Changing user interface through Streamlit [2]
  • Enhancing the Heuristic Miner [12] with additional node filtering by using the spm metric [13]
  • Implementing Fuzzy Miner as additional process mining algorithm [3], [4]
  • Implementing Alpha Miner as additional process mining algorithm [5]
  • Implementing Inductive Miner as additional process mining algorithm [6]
  • Implementing process mining algorithms which act like existing tools [7] [8]
  • Decision tool for which process mining algorithm fits best for a particular use case or data set [3-8] [12-13]
  • Development of an Object Centric Process Mining tool like [9], [10]

The whole work is open source, also the contribution during the bachelor thesis. This means that the code must be easily maintainable and extendable. The written code should contain unit tests as well.

The code will be hosted public on GitHub.

Because of the different topics, multiple students may work simultaneously on the same code base. This implies that at the end of the thesis, all changes must be merged into the main project (GitHub).


Recommended requirements:

Implementation in Python

Only basic frameworks (numpy, scikit-learn etc.) can be used. For other more sophisticated frameworks, a permission from supervisor is mandatory.



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[3] Okoye, K., Naeem, U., & Islam, S. (2017). Semantic fuzzy mining: Enhancement of process models and event logs analysis from syntactic to conceptual level. International Journal of Hybrid Intelligent Systems, 14(1-2), 67-98.

[4] Günther, C. W., & Van Der Aalst, W. M. (2007, September). Fuzzy mining–adaptive process simplification based on multi-perspective metrics. In International conference on business process management (pp. 328-343). Berlin, Heidelberg: Springer Berlin Heidelberg.

[5] Weerapong, S., Porouhan, P., & Premchaiswadi, W. (2012, November). Process mining using ?-algorithm as a tool (A case study of student registration). In 2012 tenth international conference on ICT and knowledge engineering (pp. 213-220). IEEE.

[6] Leemans, S. J., Fahland, D., & Van Der Aalst, W. M. (2014). Process and deviation exploration with inductive visual miner. In 12th International Conference on Business Process Management, BPM 2014 (pp. 46-50). CEUR-WS. org.





[12] Van Der Aalst, W., & van der Aalst, W. (2016). Data science in action (pp. 3-23). Springer Berlin Heidelberg.

[13] Lux, M., Rinderle-Ma, S., & Preda, A. (2018). Assessing the quality of search process models. In Business Process Management: 16th International Conference, BPM 2018, Sydney, NSW, Australia, September 9–14, 2018, Proceedings 16 (pp. 445-461). Springer International Publishing.


Further Literature:


Topic: AURORAL Academy Implementation

AURORAL is a H2020 Funded EU Project which aims to digitalize rural areas in Europe and to create smart communities. It contains a Middleware which is already used by many pilot regions across many domains in Europe.[1]

Introduction: [2]

AURORAL Academy is aimed at co-creating an agenda of enabling skills to be shared, through cooperation with and among capacity building providers, namely universities and VET centers and local/regional relevant networks.

This agenda of skills benefits from digitally- and AI-enhanced learning, enabled by AURORAL results and aligns with European agendas, platforms, and alliances, such as

This is specifically important in rural areas where the creation of new jobs and business opportunities are critical and dependent on the development of enabled skills and capacities.



Developing a Service which can be used in the AURORAL Middleware.

The Service must be discussed with the supervisor in advance if it fits a bachelor thesis topic.


Opportunity for joining an Open Call: [3]


In order to provide the potential applicants with the tool to learn how to integrate their solutions via the AURORAL middleware, the AURORAL online Academy (, where you may explore and exploit AURORAL's middleware for empowering regional ecosystems to exploit their opportunities in digitising resources, products, and services of smart communities. It is aimed at operational representatives of Smart Cities and Communities with consistent digital platforms – leaders, key technicians; digitally-based companies’ representatives; promoters of Smart Communities' services demanding strong digital basis on, for instance, media, mobility, health, farming, tourism, energy, industry, culture, governance, and others; providers of interconnectivity, including satellite, fibre, and 5G; providers of data-based services.


A certificate on the AURORAL Academy graduation from one of the project team members will give a 1-point preference for the proposal during the evaluation.

The certificate on the AURORAL Academy graduation may be helpful to receive funding for further development (Start-ups or SMEs).



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Topic: AI – Using a Large Language Model (LLM) together with Retrieval-Augmented Generation (RAG) for answering questions on own data



Develop a basic open source chatbot which can answer questions by using the LLM >>Llama 2<<[1]and the  >>RAG<< approach [2].

The chatbot should be able to answer questions based on data from documents and websites. The UI should be implemented as web UI, e.g., [3]

The code will be hosted public on GitHub. The chatbot should handle different use cases and aiming to be production ready by implementing several optimization approaches on RAG [4] – at least for a particular use case.


Recommended requirements:

Implementation in Python

Interest in building LLM AI solutions



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[2] Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., ... & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, 33, 9459-9474.



Topic: Individual

It is also possible to submit individual topics. Doing so, please submit an abstract to your supervisor via email. If the topic fits the requirements for a bachelor thesis, the scope of the work is agreed with the supervisor at the beginning.


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