🌍📱👉COPY LINK & PASTE ON GOOGLE https://9to5mac.org/after-verification-click-go-to-download-page👈
Statistics that are easy to use even if your expertise is non-statistical. Quickly identify distributions, correlations, outliers, and missing values. Critical statistical tests include p...
🌍📱👉COPY LINK & PASTE ON GOOGLE https://9to5mac.org/after-verification-click-go-to-download-page👈
Statistics that are easy to use even if your expertise is non-statistical. Quickly identify distributions, correlations, outliers, and missing values. Critical statistical tests include paired t-tests, one and two-proportion tests, normality tests, chi-square tests, and non-parametric tests.
Size: 1.59 MB
Language: en
Added: Apr 19, 2025
Slides: 42 pages
Slide Content
Neo4j Inc. All rights reserved 2023
Dr. Jesús Barrasa,Head of Solutions Architecture EMEA
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The World
Is Changing
2
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THE WORLD IS CHANGING
Massive Technology
Trends Are Transforming
Industries
3
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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 networkor a critical device in a network
A star…
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or a community of people
A cloud…
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Graph creates a
connected viewof data
that is intuitive and
actionable.
9
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The Property Graph: Simply Powerful
Employee CityCompany
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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Cypher (GQL): Pattern Based
MATCH(p:Employee {employee_ID: 1}) -[r:WORKS_AT*..3]-(c:Company)
RETURNc.name as company, count(*) as strength
ORDER BYstrength DESC
Node
Pattern
Relationship
Pattern
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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
13
Data Graph Knowledge Graph
Dynamic contextRaw 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 drivesinnovation
16
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An Unfair Advantage
Tech giants have long used graphs
for breakthroughs. But graphs are no longer
exclusively theirs.
17
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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
18
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
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
21
Graph Data at Scale
●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
22Neo4j Inc. All rights reserved 2024
Neo4j Inc. All rights reserved 2024
GenAI is predicted to boost global
GDP by 7% whiledoubling productivity
improvementsover the next decade
TheNext Growth Engine
23
Hallucinates
Limited
input sizes
for
context
window
Lack
of enterprise
domain
knowledge
Inability
to verify
answers
Sensitive
to prompt (input)
phrasing
Ethicaland
data bias
concerns
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Limitations of GenAI
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GenAI Needs Knowledge Graphs
LLMs
26
KGs +
Better Together
Knowledge
Facts
Context
Language
Statistics
Creativity
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1
2
3
LLMs forLanguage Generation
RAG-basedGenAI Applications
GenAI Integrations
Knowledge GraphConstruction and GraphRAG
27
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1
LLMs forLanguage Generation
GenAI Integrations
2
3
RAG-basedGenAI Applications
Knowledge GraphConstruction and GraphRAG
28
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Generate
Personalized
Natural Language
Experiences
29
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2
3
RAG-basedGenAI Applications
GenAI Integrations
1
LLMs forLanguage Generation
3
Knowledge GraphConstruction and GraphRAG
30
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Grounding With
Retrieval Augmented
Generation (RAG)
31
Knowledge graphs enable search with
explicit and implicit (vector) relationships
32Neo4j Inc. All rights reserved 2024
Why can’t I just use standard vector
databases for knowledge lookup?
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Vectors act as an
integration point
between geometric
and topological
worlds
34
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3
GenAI Integrations
Knowledge GraphConstruction and GraphRAG
2
RAG-basedGenAI Applications
1
LLMs forLanguage Generation
35
Historically, creating
a knowledge graph
from unstructured
textwas difficult
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
37Neo4j Inc. All rights reserved 2024
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”
38Neo4j Inc. All rights reserved 2024