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.

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Master thesis topics



Bachelor theses topics

Supervision Univ.-Prof. Dr. Erich Schikuta

Topic: Mobile app: "How to play volleyball"

Goal: Design and implement a small smartphone app for volleyball training. The app will be used to view pre-existing training/educational material and do quizzes. The project focuses on both design and programming of the app, whereas the contents to be presented to the user are provided by the Institute of Sport Science. A thoughtful, well-documented architecture with a clean separation between content and functionality is desired.

Number of students: 1

Recommended requirements: Java, interest in app development for Android, previous knowledge in app development is a plus.

Supervisor: Erich Schikuta (  

Co-supervisor: Jonas Bischofberger (Institute of Sport Science, )


Topic: Novel Performance Indicators for Dribbles in Football

Goal: Design and implement one or more innovative performance indicators for evaluating dribblings in football using positional data. This topic expands on a previous thesis which implemented a prediction model for the success of a dribble based on features like dribbling length, angle and position. In the next step, we want to identify players who outperform expectations due to their superior dribbling skills. If designed well, such a metric will be an innovative tool for scouting and performance analysis in football.

The available data is a list of dribbles (and other actions) + the XY position of the players and the ball during the entire game. The metric has to be implemented in Python and will be applied to and validated against real-world data from an elite football team.

Number of students: 1-2

Recommended requirements: Python. Some affinity for football may be helpful.

Supervisor: Erich Schikuta (  

Co-supervisor: Jonas Bischofberger (Institute of Sport Science, )


- Leal, K., Pinto, A., Torres, R., Elferink-Gemser, M.,  & Cunha, S. (2022). Characterization and analyses of dribbling actions in soccer: a novel definition and effectiveness of dribbles in the 2018 FIFA World Cup Russia. Human Movement, 23(1), 10-17.

- Pappalardo, L. & Cintia, P. (2017). Quantifying the relation between performance and success in soccer. Advances in Complex Systems. 21. 10.1142/S021952591750014X. 

Topic: DAPlayground

Goal: Extensions to the already existing DAPlayground system. DAPlayground is a modern, web-based e-learning platform for of data structures and algorithms visualizing graph and sorting algorithms, and tree and hash structures.

The package has to be extended in several ways: adding of missing modules, adapting user interface, serverless mode by app, …

Specific workload will be distributed among students

Number of students: 2-3

Recommended/Required Competences: Javascript, Node.js, Vue.js, and dependent technology stac

Supervisor: Erich Schikuta (  

Remarks: DAPlayground system is already existing (see and Bachelor thesis of Frick and Wegscheider (

Topic: Extension of Smart Media Presenter

Goal: The Smart Media Presenter is a very nice tool for the presentation of media resources. It allows for the creation, management, and running of presentations of images and videos. The tool was developed as part of a Bachelor thesis. Goal of this project is to extend the existing tool
by further functionality and correcting some glitches.

Number of students: 1

Recommended/Required Competences: JS + Electron

Supervisor: Erich Schikuta (  

Remarks: Smart Media Presenter is already existing (see and Bachelor thesis of Lukas Jäger (

Supervision Marian Lux

Topic: Fuzzy miner visualization tool for process
models based on event logs as input data

Goal: Development of a visualization tool for process models based on the fuzzy miner.
Users can set different parameters (e.g., filters) for visualizing the process models. The
process model nodes and edges are visualized by using different colors, sizes and
thicknesses, based on their importance in the process model. Also synthetic start and end
nodes are planned to improve the usability for end users. The visualization tool should be
able to import the event logs from an SQL data base and as well from a CSV file.

Number of students: max. 2

Recommended requirements: The frontend for setting parameters and importing data is
implemented by using Python or a web framework. The process model visualization
(colors, synthetic start- and end nodes etc.) is implemented by using Graphviz or an
interactive web framework
The backend (algorithm and business logic for import service) is implemented in Python or

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). Springer, Berlin, Heidelberg.

Supervisor: Marian LUX ((

Topic: Trimmed k-means clustering framework in

Goal: Implementation of the Trimmed k-means clustering algorithm for multiple dimensions
(including as well one dimensional data) in Python. The results are visualized in plots and
measured with quality metrics, (e.g., Silhouette Coefficient). The algorithm can be used
like a framework.
Recommended requirements: Implementation in Python.
Inspiration from R implementation:

Supervisor: Marian LUX ((

Topic: Kernel k-means clustering framework in Python

Goal: Implementation of the Kernel k-means clustering algorithm for multiple dimensions
(including as well one dimensional data) in Python. The results are visualized in plots and

measured with quality metrics, (e.g., Silhouette Coefficient). The algorithm can be used
like a framework.

Number of Students: 2

Recommended requirements: Implementation in Python.
Dhillon, I. S., Guan, Y., & Kulis, B. (2004). Kernel k-means: spectral clustering and
normalized cuts. In Proceedings of the tenth acm sigkdd international conference on
knowledge discovery and data mining (pp. 551–556).

Supervisor: Marian LUX ((

Topic: Interactive Process Visualization of Correlation
Based Customer Journey Processes in the Tourism

Goal: Implementing an interactive data visualization tool which shows processes and
findings based on correlations. Already trained models by using machine learning
technologies (tourism domain and synthetic) are provided. The trained models contain
correlations based on visited pages from a touristic web application and environmental
data (weather) for a given period of time and location.
The trained models have to be used for discovering process models and for showing
findings by filtering weights, combining different features and post processing the weights
and features (e.g., calculating probabilities). These discovered processes and findings
have to be visualized. There is a special focus on usability and interactive visualization
Recommended/Required Competences: Advanced programming skills.

Number of Students: 2

Supervisor: Marian LUX ((