This document discusses using Neo4j for logistics and parcel routing applications. It describes a large logistics carrier that needed a new parcel routing system to handle up to 5 million parcels per day with real-time routing capabilities. Neo4j was able to provide the required extreme availability, performance, and scalability through its graph database approach since a logistics network can be modeled as a graph. Queries were also simplified compared to relational databases for the complex routing needs. The flexible graph data model could also better reflect real-world data variations.

1. Graph Talk
München
Holger Temme
Area Director Central + Southern Europe

3. Node
Relationship

4. complexity = f(size, semi-structure, connectedness)
The Real Complexity

5. Connectedness in data
Lots of normalized
relationships between
the different entities,
forces developers to do
• Deep joins
• Recursive joins
• Pathfinding operations
• “open-ended” queries

6. What are graphs good for?
Complex querying
of
big volumes
of
connected data

7. So what is a graph database?
• Neo4j is a OLTP database - capable of handling
very complex analytical queries in real time
• Model, store, manage data as a graph

8. Logistics

9. Logistics
Neo Technology,Inc Confidential
Background
•One of the world’s largest logistics carriers
•Projected to outgrow capacity of old system
•New parcel routing system
•Single source of truth for entire network
•B2C& B2Bparcel tracking
•Real-time routing:up to 5M parcelsper day
Business problem
•24x7 availability,year round
•Peak loads of 2500+ parcels per second
•Complex and diverse software stack
•Need predictable performance & linear
scalability
•Daily changes to logistics network:route from
any point,to any point
Solution & Benefits
•Neo4j provides the ideal domain fit:
•a logistics network is a graph
•Extreme availability & performance with Neo4j
clustering
•Hugely simplified queries,vs.relational for
complex routing
•Flexible data model can reflect real-world data
variance much better than relational
•“Whiteboard friendly” model easy to understand
Industry: Logistics
Use case: Parcel Routing

10. Logistics
Neo Technology,Inc Confidential
Background
•One of the world’s largest logistics carriers
•Projected to outgrow capacity of old system
•New parcel routing system
•Single source of truth for entire network
•B2C& B2Bparcel tracking
•Real-time routing:up to 5M parcelsper day
Business problem
•24x7 availability,year round
•Peak loads of 2500+ parcels per second
•Complex and diverse software stack
•Need predictable performance & linear
scalability
•Daily changes to logistics network:route from
any point,to any point
Solution & Benefits
•Neo4j provides the ideal domain fit:
•a logistics network is a graph
•Extreme availability & performance with Neo4j
clustering
•Hugely simplified queries,vs.relational for
complex routing
•Flexible data model can reflect real-world data
variance much better than relational
•“Whiteboard friendly” model easy to understand
Industry: Logistics
Use case: Parcel Routing

11. Fraud Analysis

12. Traditional Approaches
Gartner’s Layered Fraud Prevention Approach (4)
(4): Gartner at http://www.gartner.com/newsroom/id/1695014

13. Discrete Data Analysis Connected Analysis
(4): Gartner at http://www.gartner.com/newsroom/id/1695014
Gartner’s Layered Fraud Prevention Approach (4)

14. Access Control

15. Network Impact Analysis

16. Social Network

17. Recommendations

18. Operational efficiency
• Minutes to millisecond performance for
specific query patterns
– Savings on HW/SW costs!
– Graph locality = predictability
• Enterprise-ready software
– Proven technology: in production for >10y