Graph Databases - The Best Kept Secret for Effective AI

Overview

Explore how connected data enhances AI accuracy and efficiency. Learn about real-world applications in space engineering and journalism, showcasing the power of relationship-focused approaches for informed predictions.

Syllabus

• Introduction to Graph Databases and AI
Overview of graph databases
Benefits of graph data structures for AI
Comparison with traditional databases
• Graph Theory Fundamentals
Nodes, edges, and properties
Understanding connectedness and traversal
Key graph algorithms (e.g., shortest path, clustering)
• Graph Database Technologies
Overview of popular graph database systems (e.g., Neo4j, Amazon Neptune)
Query languages for graphs (e.g., Cypher, Gremlin)
• Relationship-Focused Data Modeling
Designing data models to optimize AI performance
Identifying and representing relationships
• Enhancing AI with Connected Data
How graph databases improve AI accuracy
Efficiency gains with relationship-focused data
• Real-world Applications
Case study: Space engineering (satellite communication networks, mission data analysis)
Case study: Journalism (investigative reporting, content recommendation)
• Integrated Systems and Tools
Connecting graph databases with AI technologies
Tools for visualization and data analysis
• Building Intelligent Systems with Graph Databases
Designing AI workflows using graph databases
Predictive modeling with enhanced data connectivity
• Challenges and Considerations
Scalability and performance issues
Data privacy and security in graph databases
• Future Trends
The evolving role of graph databases in AI
Emerging technologies and innovations in connected data
• Conclusion and Next Steps
Summary of key learnings
Additional resources for further exploration in graph databases and AI.

---

Subjects

Conference Talks