Dr Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Mastering Windows 7 A Comprehensive Guide for Power Users .pdf
GraphSummit Milan & Stockholm - Neo4j: The Art of the Possible with Graph
1. Neo4j Inc. All rights reserved 2023
Dr. Jesús Barrasa, Head of Solutions Architecture EMEA
2. Neo4j Inc. All rights reserved 2024
The World
Is Changing
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THE WORLD IS CHANGING
Massive Technology
Trends Are Transforming
Industries
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Neo4j Inc. All rights reserved 2024
To thrive in this new
world, your organization
must derive insights from
data to create knowledge
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Explore the connections in your data to
unlock deeper insights
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A ring…
or a money laundering scheme
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r a critical device in a network
or a critical device in a network
A star…
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or a community of people
A cloud…
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Neo4j Inc. All rights reserved 2024
Graph creates a
connected view of data
that is intuitive and
actionable.
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Neo4j Inc. All rights reserved 2024
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The Property Graph: Simply Powerful
Employee City
Company
Nodes represent
objects (nouns)
Relationships are directional
Relationships connect nodes are
represent actions (verbs)
Relationships can have
properties (name/value pairs)
Nodes can have properties
(name/value pairs)
name: Amy Peters
date_of_birth: 1984-03-01
employee_ID: 1
:HAS_CEO
start_date: 2008-01-20
:LOCATED_IN
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Neo4j Inc. All rights reserved 2024
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Cypher (GQL): Pattern Based
MATCH (p:Employee {employee_ID: 1})-[r:WORKS_AT*..3]-(c:Company)
RETURN c.name as company, count(*) as strength
ORDER BY strength DESC
Node
Pattern
Relationship
Pattern
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Neo4j Inc. All rights reserved 2024
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https://www.iso.org/standard/76120.html
“Neo4j Welcomes
New GQL
International
Standard in Major
Milestone for
Database Industry”
https://neo4j.com/press-releases/gql-standard/
SAN MATEO, Calif. – April 17, 2024
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Revealing patterns in data for
deeper insight and knowledge
+
Relationships
+
Domain
Terminology
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Data Graph Knowledge Graph
Dynamic context
Raw facts and figures Deep dynamic context
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Chapters on:
• Pattern detection
• Dependency
• Identity
• Semantic search
• Enrichment with GDS
• Graph native ML
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Real-time fraud
detection
Supply chain
resilience
Customer
experience
Digital Twins
Drug
discovery
Digital twin
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Uncovering knowledge from data drives innovation
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An Unfair Advantage
Tech giants have long used graphs
for breakthroughs. But graphs are no longer
exclusively theirs.
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Helping solve the world’s most challenging
problems across every industry
Financial
Services
Telco Retail Manufacturing Life
Sciences
Technology Logistics Government
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19. We found Neo4j to be literally thousands of times
faster than our prior MySQL solution, with queries
that require 10-100 times less code.
- Dr. Volker Pacher, Senior Staff Architect at eBay
20. Deployed a Neo4j-powered
digital twin of London's
intricate transport
network, enabling real-
time incident response to
cut congestion and drive
significant economic
savings.
200%
Fraud detection
improvement
Losses prevented
annually
$40M+ 2.2B
Relationships
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Autonomous Clustering
Easy, automated horizontal scale-out
Composite Databases
Federated queries and sharded graphs
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Graph Data at Scale
22. ● Automated upgrades
● Zero maintenance
● Scalable and elastic, on-demand
● Enterprise-grade security
● High availability
● Simple pricing, consumption-based
● Procure through Aura Console or via
Cloud Marketplace
Cloud Scale
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GenAI is predicted to boost global
GDP by 7% while doubling productivity
improvements over the next decade
TheNext Growth Engine
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GenAI Needs Knowledge Graphs
LLMs
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KGs +
Better Together
Knowledge
Facts
Context
Language
Statistics
Creativity
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LLMs for Language Generation
RAG-based GenAI Applications
GenAI Integrations
Knowledge Graph Construction and GraphRAG
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1 LLMs for Language Generation
GenAI Integrations
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3
RAG-based GenAI Applications
Knowledge Graph Construction and GraphRAG
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Generate
Personalized
Natural Language
Experiences
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RAG-based GenAI Applications
GenAI Integrations
1 LLMs for Language Generation
3 Knowledge Graph Construction and GraphRAG
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Grounding With
Retrieval Augmented
Generation (RAG)
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32. Knowledge graphs enable search with
explicit and implicit (vector) relationships
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33. Why can’t I just use standard vector
databases for knowledge lookup?
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Neo4j Inc. All rights reserved 2024
Vectors act as an
integration point
between geometric
and topological
worlds
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GenAI Integrations
Knowledge Graph Construction and GraphRAG
2 RAG-based GenAI Applications
1 LLMs for Language Generation
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37. GraphRAG: Unlocking
superior reasoning with
data relationships
1. Ingest Text Data
2. Generate Knowledge Graph
3. Import Into Graph Database
4. Create Semantic Hierarchies
5. Augment Retrieval
6. Deeper Understanding
37 Neo4j Inc. All rights reserved 2024
38. GraphRAG: Unlocking
superior reasoning with
data relationships
1. Ingest Text Data
2. Generate Knowledge Graph
3. Import Into Graph Database
4. Create Semantic Hierarchies
5. Augment Retrieval
6. Deeper Understanding
"Initial results show that GraphRAG
consistently outperforms baseline RAG”
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