Neo4j GraphDay Munich Life & Health Sciences Bruno Ungermann, Neo4j

1. Welcome!
bruno.ungermann@neo4j.com
Neo4j Graphday: Health & Life Sciences

2. 9.00- 9:30 Breakfast & Networking
9.30- 12.30 Presentations
Introduction to Graph Databases and Neo4j
Bruno Ungermann, Neo4j
The Germany Centre of Diabetes Research Greatly Improves Research Capabilities with Graph Technology
Dr. Alexander Jarasch, Deutsches Zentrum für Diabetesforschung
Big Data in Genomics: How Neo4j enables personalized therapies
Dr. Martin Preusse, Knowing Health
Neo4j Bloom – Visualization & Analysis for Everyone
Michael Hunger, Neo4j
12.30 Lunch Break
How to Make your Graph Project a Success with Neo4j
Stefan Kolmar, Neo4j
Workshop: New Possibilities in Health & Life Sciences with Graphs
Michael Hunger, Dr. Martin Preusse
15.30 – Coffee & Open Discussion
Agenda Health & Life Sciences

3. Complexity

4. Connectedness

5. Bootcamp

6. Domain Model Logistics Process

7. Traditional Approach: Fixed Schema, Tables

8. Graph Model: Nodes & Relationships
Containe
r
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
LoadContainer
LoadContainer
Load
Parcel
Weight
15.5kg
Container
Load

9. Intuitiveness

10. Flexibility: no fixed schema

11. Flexibility & Agility

12. “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

13. Graph Based Success

14. 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
• ~250 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 10M+ downloads,
• 250+ 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

15. 15
• 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

16. 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

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

19. 19
Biological and Medical Knowledge in heterogeneous networks

20. 20
Biological and Medical Knowledge in heterogeneous networks

21. 21

22. 22
Medical Research
Background
• Italian research center that analyzes cancer
samples from around the world
• Provides state-of-the-art therapeutic and
diagnostic cancer services
Business Problem
• Develop a tool that provides cancer data
insights, tracks workflows and is available to
external researchers
• Relational databases didn’t provide adequate
flexibility
Solution and Benefits
• Easily find complex research data relationships
• Develop complex semantics for genomic
knowledge
• Cancer research is accessible to external
scientists

23. 23
Pharmaceutical Research
Business Problem
• Seeking to automate phenotype, compound
and protein cell behaviour research by using
previously documented research more
effectively
• Text mining for research elements like DNA
strings, proteins, RNA, chemicals and diseases
Solution and Benefits
• Found ways to identify compound interaction
behaviour from millions of rearch documents
• Relations between biological entities can be
identified and validated by biological experts
• Still very challenging to keep up to date, add
genomics data, and find a breakthrough
Background
• 5 year long drug discovery research
• Parse & Navigate over 25 Million scientific papers
• Sourced from National Library of Medicine and
tagging of “Medical Subject Headers” (MeSH tags)

24. 24
Agriculture
Background
• One of the world’s largest agribusinesses
• Founded in 1901 and based in St. Louis
• Grew from pioneer to leader in genetically
modifying plants and building related businesses
• Among the first companies to genetically modify
a plant cell (1983)
Business Problem
• Although the data volume was not huge, (200
GB, 800 Mln nodes, Bln relationships) queries
from connected data sets using traditional
technology ran for long durations. In some
cases, Monsanto had to stop them
• Shorten new product development pipeline by
one year through “yield testing in the lab”
• Efficiently impute genotypes of newly bred
populations from analysis of decades of genetic
ancestry data

25. 25
Large Chemical Company: R&D Knowledge Solution
Background
• Provide new ways to search and interact with
internal R&D Knowledge and published scientific
information, highly connected at fact level to
make knowledge actionable
• Thousands of employees in R&D
• Chemicals, Reactions Biologicals, physical-
chemical properties
Company
• 10.000+ employees in R&D
• 70+ R&D locations
• 800 new patents
• 3.000 R&D projects
• 2 Bln R&D budget

26. 26
Large Pharmaceutical Company: Enterprise Search
Background
• Personalized Search for 100.000+ employees
• 300.000.000 docs, pptx, pdf, html
• 1 Mln products
• 130.000 projects
• Sources Exchange, Sharepoint, Office 365,
Oracle, Hana, Blogs, Active Directory …..
Background
• 150.000+ employees, 300 locations

27. White Board Session