Current Topics for Theses
The possibility of writing a Bachelor's or Master's thesis is explicitly not limited to the following topics. They merely give an indication of the range of topics.
- Biases in Machine Learning: Machine learning is almost always based on data created by humans. Thus, this data is subjective and the learned knowledge of the "artificial intelligence" is biased (Prof. Christian Janiesch).
- Explainable AI: Work up and work with the most common XAI techniques and XAI tools. If applicable, implementation of visual components for new use cases such as process management (Prof. Christian Janiesch).
- Artificial Intelligence in Industry 4.0: The potential applications of artificial intelligence for optimizing operational production are wide-ranging, and practical implementations are steadily increasing, for example in robotics, process automation, and machine communication (Philip Stahmann).
- Business value of BPM innovations in practice: BPM encompasses a large number of different innovations such as process mining or RPA. Due to the high innovative power of the respective technologies, there is often little research into the actual benefits that arise in practical application. The goal of this work is to provide an overview of the business value in the practical application of a specific technology (structured literature analysis and/or interviews) (Alexander Mayr).
- Development of a BPM Lab: Installation of BPM systems, implementation of demo processes, documentation (Christian Janiesch).
- Advanced analytics in a socio-technical context: Effects of predictive and prescriptive analysis results on cognition and emotions of users of information systems can provide information about acceptance and diffusion of information systems (Philip Stahmann).
- Human-AI Interaction and Hybrid Intelligence: Humans have to interact with AI systems at many points. This may require new processes and forms or patterns of collaboration (Prof. Christian Janiesch).
- Technology Acceptance:
- The Unified Theory of Acceptance and Use of Technology (UTAUT) represents an established model in acceptance research, which is usually extended depending on the technology under investigation. The aim of this work is to present the influence of the respective constructs on AI-based technologies in a structured way (Structured literature analysis) (Alexander Mayr).
- Demographic factors such as age and gender can play a key role in technology adoption. The aim of this paper is to provide an overview of the potential of these factors on the adoption of AI-based (Structured Literature Review) (Alexander Mayr).
- Dashboard Development:
- Dashboards can be used to support human-machine interaction. The goal is the prototypical implementation of such a system (Maximilian Nebel).
- Identification, formulation and evaluation of design criteria for the support of human-machine interaction (Maximilian Nebel).
- Process mining procedure model: Based on standard process models like CRISP-DM, a model for the procedure in process mining projects can be developed (Prof. Christian Janiesch).
- Process Mining for non-process-aware systems: Process mining relies on event logs from process-aware information systems. However, many systems used in practice are not programmed to create corresponding event logs. The goal of this work is to provide an overview of the current possibilities and challenges of mining non-process-aware systems (Structured Literature Review) (Alexander Mayr).
- Event Logs: Process-aware information systems archive activities and events in event logs. In practice, these event logs are subject to some imperfections. This can lead to a limitation of their usability. The goal of this thesis is to give an overview of the technical possibilities as well as the current challenges regarding the improvement possibilities of event logs (Structured Literature Analysis) (Alexander Mayr).
- Development of a process mining lab: Installation of process mining systems, implementation of demo processes, documentation (Christian Janiesch).
- Management of RPA: Practical relevance, cost-effectiveness, selection decision, implementation, maturity models, success stories, and technological debt of RPA (Prof. Christian Janiesch).
- Machine Learning in RPA: Development of RPA bots that bring flexibility to RPA application scenarios by integrating machine learning techniques and thus overcome existing limitations of RPA (Prof. Christian Janiesch).
- RPA implementation: Modeling and automation of a BPMN model to optimize human-machine interaction (Maximilian Nebel).
- Development of an RPA Lab: Installation of RPA systems, implementation of demo bots, documentation (Christian Janiesch).
- The development of sensor technology and the associated use in industrial production enable access to status data in real time. This creates potential for data analysis, such as the detection of anomalies without time delays (Philip Stahmann).
- Learning Classifier Systems are able to classify data on the basis of evolutionary algorithms. This results in potentials in real-time classification, e.g. for anomaly detection (Philip Stahmann).
- The availability of high-quality training data is a common problem in machine learning in operational settings, including real-time data classification for anomaly detection. Few shot learning approaches try to obtain the best possible training results for artificially intelligent models with limited data (Philip Stahmann).
- The goal is to identify applicable means to appropriately handle anomalies in the context of Industry 4.0 (Maximilian Nebel).
- Generating an overview and collection of anomaly types in sensor data (Maximilian Nebel).
Furthermore, we supervise theses in cooperation with companies such as: viadee Unternehmensberatung AG, MotionMiners GmbH, thyssenkrupp Industrial Solutions AG or e.venture consulting GmbH.
Theses can also be written in cooperation with Queenland University of Technology (QUT) in Brisbane, Australia. Thesis topics will be agreed upon between the candidate, Prof. Rosemann, and Prof. Janiesch and will focus on the "algorithmic" or the "trusted enterprise", emphasizing the interface between computer science and management, especially in the areas of digitalization and artificial intelligence. If you are interested, please contact Prof. Janiesch directly outside the usual application cycle for theses.