DBIS

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 (KGs) provide structured representations of entities and their relationships, offering a powerful way to enhance recommendation systems by integrating domain knowledge, user-generated content, and external tourism data. Additionally, Large Language Models (LLMs) can be used to enrich the KG with semantic understanding, improving recommendations by leveraging context-aware reasoning and natural language interactions.

This thesis explores the development of a knowledge graph-based tourism recommendation system for Chinese travelers by constructing a time-sensitive ontology, integrating tourism data from Xiaohongshu (Red Notes), and leveraging LLMs for enhanced personalization and recommendation generation.

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 from factual inconsistencies, limited domain knowledge, and difficulties in handling complex medical terminology. Knowledge graphs (KGs) provide structured domain-specific information, offering an opportunity to enhance VLMs with prior medical knowledge. Integrating knowledge graphs into vision-language models can improve the accuracy and interpretability of generated radiology reports by ensuring consistency with known medical facts and terminology. This thesis investigates how knowledge graph-enhanced VLMs can improve the quality, factual correctness, and clinical relevance of automated radiology report generation.

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 threats in smart grids. The DSS incorporates various decision-making methodologies to evaluate and prioritize response strategies. By leveraging a co-simulated environment, the system enables realistic scenario testing, ensuring a comprehensive assessment of different countermeasures. The impact of various decision-making techniques will be analyzed and compared to existing response playbooks, providing insights into optimizing cyber resilience in smart grids.

We are looking for a highly motivated master’s student to work on an innovative project for their master’s thesis as soon as possible. The project involves investigating existing decentralized oracle networks (DONs) for verifiable credential (VC) verification and evaluating their integration with Self-Sovereign Identity (SSI) infrastructure.

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 the Blockchain4DataMarketPlace will enable transparent tracking of the detection process and its results, enhancing the overall trust and accountability in manufacturing environments.

Researching the potential for custom query functions for SPARQL with WebAssembly.

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 Owners and Data Scientists.