DBIS

Generative AI unlocks new potentials supporting tasks in many fields – including data management. It can significantly enhance data management by automating and improving tasks across the data lifecycle. In research data management, it supports metadata creation, documentation, and data curation. For database modeling, it enables schema generation from natural language, query formulation, and schema evolution. In data integration, it assists with semantic mapping and knowledge graph construction. It also can contribute to data quality and governance through supporting anomaly detection and policy drafting and may enhance data ecosystems by aiding cataloging and data exchange. However, the integration of generative AI into data management also presents challenges—ranging from resource efficiency and result quality to issues like bias, hallucination, and data privacy. In this seminar, you will explore a specific subtopic within this field, deepening your understanding while developing your academic writing and presentation skills.