The Past, Present, and Future of Graphs and Connected Data

Overview

Explore the power of graph databases and connected data, from space engineering to investigative journalism, with insights on future applications in AI and machine learning.

Syllabus

• Introduction to Graphs and Connected Data
Definition and significance of graphs
Overview of connected data concepts
Importance in various domains
• The History of Graph Theory and Databases
Historical development of graph theory
Evolution of graph databases
Key milestones in graph technology
• Graph Databases Overview
Comparison to relational databases
Types of graph databases (e.g., property graphs, RDF)
Popular graph database technologies (e.g., Neo4j, Amazon Neptune)
• Applications of Graphs in Modern Industries
Graphs in space engineering
Use cases in investigative journalism
Graphs in financial services and fraud detection
Healthcare applications and bioinformatics
• Advanced Graph Data Management
Graph data modeling techniques
Querying graph databases (e.g., Cypher, Gremlin, SPARQL)
Indexing and optimization strategies
• Machine Learning with Graphs
Introduction to graph-based machine learning
Graph embeddings and feature extraction
Graph Neural Networks (GNNs) and their applications
• Future Trends in Graphs and Connected Data
Innovations in graph technology
The role of graphs in AI advancements
Predictions for future applications and research areas
• Case Studies
In-depth exploration of real-world graph database implementations
Lessons learned and impact analysis
• Ethical and Privacy Considerations
Handling sensitive data in graph databases
Ensuring privacy and compliance
Ethical considerations in data connectivity and AI
• Course Summary and Future Directions
Key takeaways from the course
Emerging research questions
Resources for continued learning

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Subjects

Conference Talks