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Department of Computer Science
Enterprise Computing

Current Topics for Theses

The possibility of writing a Bachelor's or Master's thesis is explicitly not limited to the following topics. These merely give an indication of the range of topics.

Topic Overview

  • 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 analytic results on cognition and emotions of users of information systems can provide information about acceptance and diffusion of information systems. (Philip Stahmann)
  • AI Mindfulness, 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)
  • Dashboard Development:
    • Dashboards can be used to support human-machine interaction. The goal is the prototypical implementation of such a system (Maximilian Nebel).
    • Design criteria for supporting human-machine interaction will be identified, formulated and evaluated (Maximilian Nebel).
  • 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 Industrie 4.0: The application potentials of artificial intelligence for optimizing operational production are broad, the practical implementations are steadily increasing, e.g. in robotics, process automation or machine communication. (Philip Stahmann)
  • Procedure Model for Process Mining: Based on standard procedure models such as CRISP-DM, develop a model for the procedure in process mining projects. (Prof. Christian Janiesch)
  • Development of a Process Mining Lab: Installation of process mining systems, implementation of demo processes, documentation (Christian Janiesch).
  • Management of RPA: Practical relevance, profitability, selection decision, implementation, maturity models, success stories as well as technological debts of RPA. (Prof. Christian Janiesch)
  • Machine Learning and RPA: Development of RPA bots, which bring flexibility to RPA application scenarios by integrating machine learning techniques and thus overcome existing limitations of RPA. (Prof. Christian Janiesch)
  • Implementation of RPA: 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 potentials in data analysis, such as the detection of anomalies without time delays. (Philip Stahmann)
  • Learning Classifier Systems are able to classify data based on 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 practice, so also in real-time data classification for anomaly detection. Few shot learning approaches try to elicit 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 Industrie 4.0. (Maximilian Nebel)
  • Generating an overview and collection of anomaly types in sensor data. (Maximilian Nebel)

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 "trusted enterprise", emphasizing the interface between computer science and management, especially in the areas of digitalization and artificial intelligence. If interested, please contact Prof. Janiesch directly outside the usual dissertation application cycle.