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Start 5 June 2026 14:11

Einde 5 June 2026

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Graph Databases - The Best Kept Secret for Effective AI

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.
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23 minutes

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Overzicht

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.

Lesprogramma

  • 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.

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