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

Supervision Univ.-Prof. Han van der Aa, Ph.D.

Application instructions: When applying for projects with Prof. Van der Aa, please send an e-mail including a transcript of records and short CV in your e-mail. Beyond the listed topics, you are also welcome to propose your own topic or indicate general directions that interests you when applying. Note that, unless stated otherwise, all listed research directions can be scoped to be suitable for P1, P2, and master projects. Ideally you work on the same direction from P1, through P2, up to your master project.

P1-P2-Master topic

Currently there are no projects available for the 2025 Winter Semester.

Bachelor thesis topics

Supervision Univ.-Prof. Dr. Erich Schikuta

Valuating passing risk and reward in football

Goal

Build a predictive model using the risk and reward of passes based on advanced football analytics models. The goal is to turn a set of given advanced metrics (DAS, xC) measuring the risk and reward of passing actions into a suitable representation, for example a graph. This representation will then be used to solve a hard and interesting predictive task within a football match, for example predicting the match outcome, predicting possession outcomes or evaluating player skill. Methods could involve deep learning techniques (e.g. Graph Neural Networks) but other approaces are possible.

The necessary tracking (XY positions) and event (timestamped actions) data for the project will be provided, as well as some foundational code to get started.

Requirements

  • Python
  • Interest and basic knowledge of football (soccer) is a plus

Number of Students: 1-2

Co-Supervision:

Related Literature

Anzer, G., & Bauer, P. (2022). Expected passes. Data Mining and Knowledge Discovery, 36(1), 295-317.

Bischofberger, J., & Baca, A. (2025). Dangerous Accessible Space: A Unified Model of Space and Value in Team Sports.https://www.researchsquare.com/article/rs-6932689/v1

 

WineCellar As A Service (cross-platform) App

Goal:

The goal of this project is to design and develop an information system for the administration and the management of a wine cellar.

Hereby, a thorough market analysis and comparison of available competing systems (also old Bachelor's theses) have to be done. Based on these results and customer interviews, a list of specific requirements has to be defined. Specific emphasis has to be laid on the system architecture allowing for a cloud-based approach. The functionality of the application to be developed has to cover all important workflows for wine cellar management, as wine inventory and cellar location management, wine purchase, personal wine information handling, wine web resource (information and purchase options) crawling, etc. Specific focus has to be laid on usability and responsive design.

IMPORTANT: A functional requirement is the development of a (possible cross-platform) app running on PC (Windows 11) and Smartphone (Android).

Specific functionality will be part of the requirements analysis.

Required Competences (mandatory technology stack):

  • Serverless solution
  • SQLite database stored in the Cloud (e.g. u:cloud)
  • The technology stack (specifically the cross platform development framework, e.g. Flutter, Kotlin, etc.) has to be decided and is up to the student. Personal experience in an existing framework is a plus.

Number of Students: 1

Resources:

Old Bachelor theses on the same topic with different technology stacks.

 

Extension of Smart Media Presenter 2.0

Goal:

The Smart Media Presenter 2.0 is a specialized 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 two Bachelor's theses.

The goal of this project is to extend the existing tool with an AI-based open-source photo enhancement module and improve some basic functionalities.

The AI-photo enhancer codebase is free to choose; however, a possibility could be Upscaly (https://upscayl.org/) with its codebase (https://github.com/upscayl/upscayl)

Any further improvement of the code base is welcomed.

Required Competences:

  • Typescript/Javascript, Electron, React.js, MaterialUI and Firebase

Number of Students: 1

Resources:

Version 1.0 (original): Lukas Jäger
Thesis: https://ucloud.univie.ac.at/index.php/s/4xylY3NL38ibQMp),
Codebase https://github.com/lksjgr/SmartMediaPresenter

Version 2.0 (actual): Wsewolod Golubkow
Thesis und Codebase: https://github.com/blubcow/SmartMediaPresenter

 

Video analysis of Dancesport clips

Goal

Competitive dancesport is an acyclic team sport (couple, formation teams) with a strong technical focus besides physical demands. The winner of a dancesport competition (tournament) is decided by agglomeration of the subjective opinion of a group of human judges. Goal of this work is the first step towards a more objective decision process by introducing an AI-based judge. Hence, this work is strongly research orientated. Starting point of this research endeavour is the identification of sportive dance objects/activities/characteristics by video analysis.

The research approach of this work uses YOLO Real-Time Object Detection and training of dance sport actions using existing dance videos/photos.

As a motivation a first and very simple detection approach is illustrated by two video clips, on the one hand an original TV video scene https://ucloud.univie.ac.at/index.php/s/z9jFL98QmoCzkBg, and on the other hand the same video scene after a detection process performed by YOLO https://ucloud.univie.ac.at/index.php/s/2H9WjPqCnGWBimx.

The following tasks have to be performed as part of the thesis:

  • Creation of a collection of dance video data by categorization (couple, formation, latin, ballroom, video position).
  • Training of YOLO modules for object identification of “female latin/ballroom dancer”, “male latin/ballroom dancer” and “couples”.
  • Development/Adaptation of a GUI for specific dancesport YOLO training/detection tasks (e.g. pure Python, YOLOSHOW, OpenCV GUI, …).

Further some questions have to be answered or at least recommendation have to be given:

  • Which YOLO version and which model to be used
  • Which quality of video necessary/recommended
  • Which supported YOLO computer vision Tasks useful
  • Analysis of the best video position

    • Center from above
    • Corner from angle (common for TV broadcasting)
    • Center from angle (common for TV broadcasting)

  • Identification of ballroom vs latin dancing
  • Identification of dances (couple/team)

Required Competences:

  • Python and its ecosystem
  • Tie to competitive dancesport is surely a plus.

Number of Students: 1

Resources:

 

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.

Supervisor: Erich SCHIKUTA, erich.schikuta@univie.ac.at

Supervision and thesis in German or English

 

­Supervision Marian Lux

Topic: AI 130261 – Develop Semantic Chunking by using a Large Language Model (LMM) to improve Retrieval-Augmented Generation (RAG)

A colorful circle with different objects around it AI-generated content may be incorrect.

Goal:

Develop a semantic chunking (inspired by [3], [4]) Python Library for RAG [2] which uses also an LLM to create chunks and is therefore an advanced method to existing ones. The Python Library is finally accessible via PyPI [1].

 

What is expected:

Text data from files or URLs are passed to the chunking method which uses a sliding context window with a LLM to create meaningful chunks for RAG. Optimizations like removing chunks with low information content, merging similar chunks or enriching chunks with metadata are encouraged, also with usage of LLMs. The created text chunks can then be stored in a vector database for RAG.

 

To evaluate the library, simple chunking techniques, containing at least fixed size-, recursive and also semantic chunking, from existing state-of-the-art frameworks (LLamaIndex[5],[6] or LangChain) are used to compare the developed chunking method.

 

The code, thesis and whole documentation will be hosted public on your own GitHub repository as open source.

 

Recommended requirements:

Implementation in Python

Access to computer hardware which can run local 4-8 B LLMs

Interest in building LLM AI solutions

 

Supervisor:

Dr. Marian LUX - marian.lux@univie.ac.at

Supervision and thesis in German or English

 

References:

[1] https://pypi.org/

[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.

[3] https://medium.com/the-ai-forum/semantic-chunking-for-rag-f4733025d5f5

[4] https://medium.com/towards-artificial-intelligence/15-rag-chunking-techniques-every-ai-engineer-should-know-adc48fee9389

[5] https://developers.llamaindex.ai/python/framework/optimizing/basic_strategies/basic_strategies/

[6] https://developers.llamaindex.ai/python/framework/optimizing/basic_strategies/basic_strategies/


Topic: AI 130262 – Developing a ReAct inspired Agent from scratch for utilizing on Large Language Models (LLMs)

A diagram of a work flow AI-generated content may be incorrect.

Goal:

Develop a ReAct (Reason + Act) inspired Agent [1],[2] from scratch by considering splitting a query into sub tasks and by providing a pool of tools to choose from to answer the query in a minimum set of iterations. (cf., image “How do autonomous agents work?” in [7]

The agent works with local open source LLMs (2-8B) where it is recommended to use the Ollama API [3][6]. Furthermore, the implementation must be easy to extend and maintain.

For evaluation of the agent, provide at least the following tools (please use only libraries without API keys):

  • Vector search for documents of a particular topic
  • Web search for current data
  • Wikipedia Search for Encyclopedia
  • Calculator for mathematical expressions

on a simple chatbot implementation (CLI interface or an existing one) and compare the agent against SOTA agent frameworks, like LlamaIndex[4] or LangChain [5].

 

The code is open-source and will be hosted public on your GitHub repository. A PyPI repository is expected as well. Therefore, the agent is used like SOTA ReAct agent framework like LlamaIndex[4] or LangChain [5].

 

Also, improvements from existing ReAct agents from previous bachelor theses are possible (discuss this with your supervisor).

 

Recommended requirements:

Implementation in Python

Interest in building LLM AI solutions

 

Supervisor:

Dr. Marian LUX – marian.lux@univie.ac.at

Supervision and thesis in German or English

 

References:

[1] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023, January). React: Synergizing reasoning and acting in language models. In International Conference on Learning Representations (ICLR).

[2] ReAct Prompting: https://www.promptingguide.ai/techniques/react

[3] Ollama: https://ollama.com/

[4] LlamaIndex ReAct Agent: https://docs.llamaindex.ai/en/stable/examples/agent/react_agent/

[5] LangChain ReAct Agent: https://python.langchain.com/v0.1/docs/modules/agents/agent_types/react/

[6] Ollama Python Library: https://github.com/ollama/ollama-python

[7] Image: How do autonomous agents work? https://medium.com/design-bootcamp/a-comprehensive-and-hands-on-guide-to-autonomous-agents-with-gpt-b58d54724d50

 

Topic: Process Mining – ProcessIntel

A computer screens with multiple screens AI-generated content may be incorrect.

Goal:

Improving/maintaining and providing new features to the open source process mining [3] tool ProcessIntel [1] which was mainly developed from previous bachelor theses and is maintained by the nonprofit research center SWISDATA.

The application can run locally but an online demo is available at https://processintel.org [5]

Different topics based on this process mining tool are possible. See [4]

The scope of the work is agreed with the supervisor at the beginning.

The whole work is open source, thus the code must be easily maintainable, extendable and fit the coding guidelines. The written code should contain unit tests and is merged via pull requests.

 

Multiple students can work simultaneously on the same code base. The written code is merged to the main git repository [1]

 

Recommended requirements:

Implementation in Python

Only basic frameworks (like NumPy) can be used. For other more sophisticated frameworks, a permission from supervisor is mandatory.

 

Supervisor:

Dr. Marian LUX - marian.lux@univie.ac.at

Supervision and thesis in German or English

 

References:

[1] https://code.swisdata.eu/SWISDATA/ProcessIntel

[2] https://github.com/MLUX-University-of-Vienna?tab=repositories

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

[4] https://code.swisdata.eu/SWISDATA/ProcessIntel/issues

[5] https://processintel.org

 

 

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.

Supervisor:

Dr. Marian LUX - marian.lux@univie.ac.at

Supervision and thesis in German or English