This thesis focuses on addressing the limitations of current Distributed Analytics Architectures by developing a declarative approach to model and automate cross-institutional analysis workflows. It aims to implement a secure, low-complexity architecture that enhances reproducibility and extensibility while evaluating its effectiveness compared to existing methods.
As software engineering continues to evolve, agile frameworks have become the main paradigm for project management and development. Among these, Scrum is widely adopted by numerous software organizations due to its iterative and incremental development approach, emphasis on team collaboration, and rapid feedback cycles. With the ongoing advancements in artificial intelligence technologies, particularly the recent ...
Technical support is an essential aspect of various industries, e.g., to provide help with maintaining machinery and IT systems. However, diagnosing error messages and faults in complex technologies can be a time-consuming and challenging task. The maintainer has to search through the long documentation booklets for the technology in order to find a solution or ...
The innovative integration of Mixed Reality and Large Language Models can lead to highly interactive instructional MR agents. Utilized as automated instructors, these MR agents have the potential to significantly enhance traditional instruction manuals by providing visual guidance. For instance, they can illustrate the next required actions in practical tasks such as tightening screws in ...
The increasing connectivity of Industrial Control Systems (ICS) has elevated the need for robust cybersecurity measures. However, evaluating the effectiveness of Intrusion Detection Systems (IDS) in ICS environments remains fragmented and inconsistent. This thesis addresses this challenge by developing a systematic, modular benchmarking environment that enables reproducible and standardized evaluation of machine learning-based IDS across ...
The goal of this thesis is to design, implement, and evaluate a sensor-based maintenance bot that uses Large Language Models (LLMs) to support predictive maintenance and decision-making. The bot should be capable of retrieving, analyzing, and reasoning over time series sensor data as well as unstructured maintenance-related documentation (e.g., technical manuals, incident reports). The result ...
In modern manufacturing, data plays a crucial role in optimizing processes, enhancing efficiency, and enabling interoperability across different systems. However, data models in manufacturing are often heterogeneous, proprietary, and lack standardization, making data sharing and integration challenging. The FAIR principles – Findability, Accessibility, Interoperability, and Reusability – provide a structured framework to improve data ...
The goal of this thesis is to investigate and define stakeholder engagement and involvement within the lifecycle of sensor-based MLOps. While MLOps principles streamline the deployment and maintenance of machine learning (ML) models, ensuring proper stakeholder involvement remains a challenge. Different stakeholders must collaborate effectively across various lifecycle stages to ensure model reliability, fairness, and ...
The proliferation of misinformation and fake news on social media platforms poses a significant challenge in today’s digital age. Traditional automated fake news detection systems often struggle with the complexity of the task, lacking the ability to provide detailed explanations and interpret nuanced contextual information.
This thesis explores the enhancement of fake news detection using Multi-Agent ...
AutoML automates machine learning pipelines, making model training accessible without deep expertise. Recent advancements use LLM-based agents to optimize pipeline steps, but existing solutions often require large-scale models with high computational costs. Smaller, open-source models provide a more accessible alternative, especially when combined with domain-specific pre-trained models. However, integrating these models into AutoML via agent ...
Das Ziel dieser Bachelorarbeit “OGRETA – Ontologie-gestützter Rahmen für das Entwicklungstracking von Arbeitskompetenzen” ist die Überführung des GRETA-Kompetenzmodells in eine Ontologie, die als Grundlage für ein Empfehlungssystem dienen kann. Das GRETA-Modell definiert die beruflichen Kompetenzen von Lehrkräften in der Erwachsenenbildung unabhängig von Fachgebiet, Erfahrung oder Beschäftigungsart. Durch die Strukturierung dieses Modells als Ontologie soll die ...
Sensor data is often unstructured and while available datasets show some clear use cases, for example calculating energy consumption over time, relationships between measurements can often go unnoticed without a thorough examination of the data. While exploratory data analysis can reveal connections, without a clear analytical direction the results may be limited to general information, ...
With the increasing popularity of personalized tourism experiences, recommendation systems play a crucial role in helping travelers discover destinations, activities, and itineraries that match their preferences. Traditional recommendation models often rely on collaborative filtering or content-based filtering, which may struggle with cold-start issues, lack of contextual awareness, and limited adaptability to dynamic tourism trends.
Knowledge graphs ...
Radiology report generation is a critical task in medical imaging analysis, where accurate and comprehensive descriptions of medical scans (such as X-ray, CT, or MRI) are required for diagnosis and treatment planning. Vision-language models (VLMs) have recently gained attention for automating this process by generating textual reports from medical images. However, standard VLMs often suffer ...
With the increasing complexity of industrial control systems (ICS) in smart grids, the risk of cyber-attacks is also rising. To enhance the security and resilience of these systems, new approaches are needed for detecting and mitigating cyber incidents. This thesis develops a decision support system (DSS) designed to assess and recommend effective countermeasures against cyber ...
The aim of this thesis is to design, implement, and evaluate a machine learning-based system for detecting chatter in thin-walled workpieces during machining processes. By leveraging MLOps principles, the system will automate the data pipeline from sensor data acquisition to model deployment, ensuring a scalable and efficient workflow. Additionally, the integration of the system within ...
Aim of the Thesis:
The aim of this bachelor thesis is to design, implement, and evaluate a blockchain-based escrow mechanism to support secure and transparent payment handling for data challenges in the Blockchain4DataMarketPlace. The proposed system will automate fund management, support dispute resolution processes, and provide transparent documentation of transactions, ensuring fairness and trust between Data ...
The growing adoption of electric vehicles (EVs) is reshaping power grids by introducing cybersecurity challenges that could be exploited to target both the EV and the power grid. This thesis focuses on vulnerabilities associated with EV charging and the effects of cyberattacks on grid stability and resilience. Of particular interest is the Open Charge Point ...
Exploring the potential of Solid Pods for the cultural sector offers an exciting opportunity to address challenges in data management, privacy, and interoperability. Solid Pods, a technology framework designed to enable individuals to store and control their data, promises transformative applications in the cultural sector. This thesis investigates how Solid Pods can be applied to ...
Large Language Models (LLM) can be applied to transform a natural language (NL)-based text input query into a NL-based text answer. A common use case are personal assistants, e.g., for learning activities. In such teaching contexts they can process knowledge recorded in plain text documents, create summarizations, or teach knowledge according to a curriculum. However, ...
