Data Skeptic Graph Databases and AI
12 snips
Oct 21, 2024 Yuanyuan Tian, Principal Scientist Manager at Microsoft Gray Systems Lab, dives into the world of graph databases and their applications. She discusses overcoming the hurdles small enterprises face in adopting this technology and the importance of the GQL project for standardization. The conversation highlights how graph databases enhance fraud detection in finance, optimize supply chains, and improve healthcare analytics. Yuanyuan also explains the role of large language models and specialized query languages in making these powerful databases more accessible.
AI Snips
Chapters
Transcript
Episode notes
Graph Databases: Intuitive Data View
- Graph databases offer an intuitive view of data as entities and relationships.
- They also provide intuitive query languages and built-in graph algorithms.
GQL: Standardization of Graph Query Languages
- Before GQL, the graph query language landscape was fragmented, with each vendor having its own language.
- GQL aims to standardize this, potentially simplifying graph database adoption.
Graph Use Cases: Fraud Detection
- Consider graph databases for fraud detection in finance and healthcare.
- Look for patterns in transaction graphs or insurance claims to identify potential fraud.