Bruno Ungermann Neo4j

1. Herzlich Willkommen!
Produktdatenmanagement
mit Neo4j
andreas.weber@s-pdm.com
bruno.ungermann@neo4j.com
1

2. Agenda
• Überblick Graph Databases, Neo4j, Use Cases (Bruno Ungermann, Neo4j)
• Produktdatenmanagement mit Graphdatenbanken (Dr. Andreas Weber, semantic PDM)
• Q&A (Chat)

3. Complexity

4. Connectedness

5. Domain Model Logistics Process

6. Traditional Approach: Fixed Schema, Tables

7. Graph Model: Nodes & Relationships
Container
Load
USING ROUTE
Depart 2014-04-15
Arrive 2014-04-28
USING_CARRIER
Vessel
Physical
Container
Shipment Carrier
Emission
Class A
Shipment:
ID 256787
Carrier:
DHL
Route
10520km
Route:
823km
Fueling
Max Wgt
80
Type Gas
B
Town:
Tokyo
Town:
Hong
Kong
Town:
Hamburg
Container
Load
Container
Load
Container
Load
Parcel
Weight
15.5kg
Container
Load

8. Intuitiveness

9. Flexibility

10. “We found Neo4j to be literally thousands of times
faster than our prior MySQL solution, with queries
that require 10-100 times less code. Today, Neo4j
provides eBay with functionality that was previously
impossible.” - Volker Pacher, Senior Developer
“Minutes to milliseconds” performance
Queries up to 1000x faster than other tested database types
Speed

11. Graph Based Success

12. Neo4j - The Graph Company
500+
7/10
12/25
8/10
53K+
100+
250+
450+
Adoption
Top Retail Firms
Top Financial Firms
Top Software Vendors
Customers Partners
• Creator of the Neo4j Graph Platform
• ~400 employees
• HQ in Silicon Valley, other offices include
London, Munich, Paris and Malmö
(Sweden)
• $160M in funding from Morgan Stanley,
Fidelity, Sunstone, Conor, Creandum, and
Greenbridge Capital
• Over 15M+ downloads,
• 350+ enterprise subscription customers
with over half with >$1B in revenue
Ecosystem
Startups in program
Enterprise customers
Partners
Meet up members
Events per year
Industry’s Largest Dedicated Investment in Graphs

13. 13
• Record “Cyber Monday” sales
• About 35M daily transactions
• Each transaction is 3-22 hops
• Queries executed in 4ms or less
• Replaced IBM Websphere commerce
• 300M pricing operations per day
• 10x transaction throughput on half the
hardware compared to Oracle
• Replaced Oracle database
• Large postal service with over 500k
employees
• Neo4j routes 10M+ packages daily at peak,
with peaks of 5,000+ routing operations per
second.
Handling Large Graph Work Loads for Enterprises
Real-time promotion
recommendations
Marriott’s Real-time
Pricing Engine
Handling Package
Routing in Real-Time

14. 14
• 27 Million warranty & service documents
parsed for text to knowledge graph
• Graph is context for AI to learn “prime
examples” and anticipate maintenance
• Improves satisfaction and equipment
lifespan
• Connecting 50 research databases, 100k’s of
Excel workbooks, 30 bio-sample databases
• Bytes 4 Diabetes Award for use of a
knowledge graph, graph analytics, and AI
• Customized views for flexible research angles
• Almost 70% of CC fraud was missed
• ~1B Nodes and Relationships to analyse
• Graph analytics with queries & algorithms
help find $ millions of fraud in 1st year
Improving Analytics, ML & AI for Enterprises
Caterpillar’s AI Supply
Chain & Maintenance
German Center for
Diabetes Research (DZD)
Financial Fraud
Detection & Recovery Top 10
Bank

15. Discrete Data
Minimally
connected data
Neo4j is designed for data relationships
Other NoSQL
Relational
DBMS
Neo4j Graph DB
Connected Data
Focused on
Data Relationships
Development Benefits
Easy model maintenance
Easy query
Deployment Benefits
Ultra high performance
Minimal resource usage
Use the Right Database for the Right Job

16. 16
Common Graph Technology Use Cases
Network & IT Operations
Application Management
Meta Data
Management
Real-Time
Recommendations
Identity & Access Mgt.
Security
Knowledge
Management
Fraud Detection, AML
Compliance, GDPR

17. How Neo4j Fits — Common Architecture Patterns
From Disparate Silos
To Cross-Silo Connections
From Tabular Data
To Connected Data
From Data Lake Analytics
to Real-Time Operations

18. 18
Some Examples of Typical Manufacturing Data
Customer Data
Personal Data
Customer
Relationships
Corporate Data
Event Data
Sensor /
Telematics Data
Warranty Claims
Customer Contact
Product
Data
Documentation
Processes
Product Details
Product
Hierarchy
Bills of Materials
3rd Party Data
Social Media
Market Data
Dealer Data
Organizational
Data
Documentation
Facilities
Processes
Systems and
Databases
KPIs and Reports
Organisational
Hierarchy
Supply Chain
Data
Supplier Data
Logistics Data
Inventory Data

19. 19 Created using https://neo4j-arrows.appspot.com/
Product 360 View Example
Can I use a knowledge graph to improve my
products and services?
Can a knowledge graph help improve
my product time-to-market?
Can I use a knowledge graph for
customer-facing use as well?

20. Supply Chain Example Graph
20
Can I use the graph to help me improve my ordering and procurement processes?
Can the graph help me save money on orders?
Can I optimize my inventory using a graph?
Can the graph help me with comparative analysis of my suppliers and their products?

21. 21
Customer 360 View Example Graph
Can I use the customer 360 view graph to improve my customers’ experience?
Can I use the customer 360 view graph to identify high Lifetime Value customers?
Can I use the customer 360 view graph to detect and prevent churn?
Can I use the customer 360 view graph to improve upsell and cross-sell?

22. Warranty Analytics Example Grap
22
Can I find patterns in the graph indicating inappropriate claims or warranty fraud?
Can I use the graph to help predict future claims?
Can the graph help me manage my warranty and recall risk?
Can I use the graph to help me understand whether faults might be caused by supplier issues?

24. Bootcamp

25. Bootcamp

27. 27
Leverage
the experts!
Don’t reinvent
the wheel

28. White Board Session