Neo4j GraphTalks

Herzlich Willkommen! !!!!

Oktober 2016!bruno.ungermann@neotechnology.com

Neo4j GraphTalks ! •  09:00-09:30 Frühstück und Networking

•  09:30-10:00 Einführung in Graph-Datenbanken und Neo4j (Bruno Ungermann, Neo4j)

•  10:00-10.30 ADAMA: From Data Sharing to Knowledge Management (Georgiana Francescotti, Digital & Knowledge Services, ADAMA AGRICULTURE B.V.)

•  10.30-11.00 ADAMA: Erfahrungswerte aus der Implementierung und Demo (Darko Krizic, CTO PRODYNA AG)

•  Open End (PRODYNA: Christoph Körner, Moritz Schmidt, Michael Joszt, Buket Oezcan, Lena Hawelky NeoTechnology: Dirk Möller, Alexander Erdl)

Berlin Metro Map

The Internet (oT)

Domain Model Logistics Process

Traditional Approach: Tables

Graph Model: Nodes & Relationships

Container !Load!

USING ROUTEDepart 2014-04-15Arrive 2014-04-28

FROM

TO

USING ROUTE

Depart 2014-04-15

Arrive 2014-04-28

FUELING

USING_CARRIER

LOAD

USING CARRIER

Vessel!

Physical !Container!

Container !Load!

Shipment!Carrier!

Emission!Class A!

Shipment!

Carrier!

Route!10520km!

Route!823km!

!

Fueling!Max Weight 80!Type of Gas B!

!

Town:!Tokyo!

Town:!Hong Kong!

Town:!Hamburg!

Container !Load!Container !

Load!Container !Load!Parcel !Weight 15.5kg!

Intuitiveness

A Naturally Adaptive Model

Flexibility

“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

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 BenefitsEasy model maintenance

Easy query

Deployment BenefitsUltra high performance Minimal resource usage

Use the Right Database for the Right Job

2000 2003 2007 2009 ! 2011! 2013! 2014! 2015!2012!

GraphConnect, first conference for

graph DBs

First Global 2000

Customer

Introduced first and only

declarative query language for

property graph

Published O’Reilly

bookon Graph Databases

First native

graph DB in 24/7

production

Invented property

graph model

Contributed first graph DB

to open source

Extended graph data model to labeled property

graph

150+ customers 50K+ monthlydownloads

500+ graph DB eventsworldwide

Neo4j: The Graph Database Leader

FinancialServices Communications Health &

Life Sciences HR &

Recruiting Media &

Publishing SocialWeb

Industry & Logistics

Entertainment Consumer Retail Information Services Business Services

Neo4j Leads the Graph Database Revolution

2012 à 2015

“Forrester estimates that over 25% of enterprises will be using graph databases by 2017”

“Neo4j is the current market leader in graph databases.”

“Graph analysis is possibly the single most effective competitive differentiator for organizations pursuing data-driven operations and

decisions after the design of data capture.”

IT Market Clock for Database Management Systems, 2014https://www.gartner.com/doc/2852717/it-market-clock-database-management

TechRadar™: Enterprise DBMS, Q1 2014http://www.forrester.com/TechRadar+Enterprise+DBMS+Q1+2014/fulltext/-/E-RES106801

Graph Databases – and Their Potential to Transform How We Capture Interdependencies (Enterprise Management Associates)http://blogs.enterprisemanagement.com/dennisdrogseth/2013/11/06/graph-databasesand-potential-transform-capture-interdependencies/

Neo4j Leads the Graph Database Revolution

Graph Based Success

NEO4j USE CASES Real Time Recommendations

Meta Data Management

Fraud Detection

Identity & Access Management

Graph Based Search

Network & IT-Operations

LogistikRDBMS CRM

RDBMS

MailsMailsyst

DokumenteFilesysem

MediaLibraryFil

esysem

CMSRDBMS

SocialRDBMS

LogFilesRDBMS

EcommerceRD

BMS

HAS

OPE

NED_

ACC

OUN

T

BRIDGES

POWERSPOWERS

INCLUDE

INCLUDE

CREATE

IN

SOUR

CE

IN

IN

TRUSTS

TRUSTS

AUTHENTICAT

ES CA

N_R

EA

D

!

Business Problem •  Optimize walmart.com user experience •  Connect complex buyer and product data to gain

super-fast insight into customer needs and product trends

•  RDBMS couldn’t handle complex queries

Solution and Benefits •  Replaced complex batch process real-time online

recommendations •  Built simple, real-time recommendation system with

low-latency queries •  Serve better and faster recommendations by

combining historical and session data

Background

•  Founded in 1962 and based in Arkansas •  11,000+ stores in 27 countries with walmart.com

online store •  2M+ employees and $470 billion in annual

revenues

Walmart RETAIL

Real-Time Recommendations!18!

!

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 networkB2C and B2B parcel trackingReal-time routing: up to 7M parcels per day

Business Problem •  Needed 365x24x7 availability •  Peak loads of 3000+ 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 and Benefits •  Ideal domain fit: a logistics network is a graph •  Extreme availability, performance via clustering •  Greatly simplified routing queries vs. relational •  Flexible data model reflect real-world data variance

much better than relational •  Whiteboard-friendly model easy to understand!

Accenture LOGISTICS

19! Real-Time Routing Recommendations!

!

Background •  Second largest communications company

in France •  Based in Paris, part of Vivendi Group, partnering

with Vodafone

Solution and Benefits •  Flexible inventory management supports modeling,

aggregation, troubleshooting •  Single source of truth for entire network •  New apps model network via near-1:1 mapping

between graph and real world •  Schema adapts to changing needs

Network and IT Operations !

SFR COMMUNICATIONS

Business Problem •  Infrastructure maintenance took week to plan due

to need to model network impacts •  Needed what-if to model unplanned outages •  Identify network weaknesses to uncover need for

additional redundancy •  Info lived on 30+ systems, with daily changes

LINKED

LINKED

DEPENDS_ON

Router Service

Switch Switch

Router

FiberLink FiberLink

FiberLink

OceanfloorCable

20!

!

Background •  Oslo-based telcom provider is #1 in Nordic

countries and #10 in world •  Online, mission-critical, self-serve system lets

users manage subscriptions and plans •  availability and responsiveness is critical to

customer satisfaction

Business Problem •  Logins took minutes to retrieve relational

access rights •  Massive joins across millions of plans,

customers, admins, groups •  Nightly batch production required 9 hours and

produced stale data

Solution and Benefits •  Shifted authentication from Sybase to Neo4j •  Moved resource graph to Neo4j •  Replaced batch process with real-time login

response measured in milliseconds that delivers real-time data, vw yday’s snapshot

•  Mitigated customer retention risks

Identity and Access Management !

Telenor COMMUNICATIONS

SUBSCRIBED_BY!CONTROLLED_B

Y!

PART_OF!USER_ACCESS!

Account!

Customer!

Customer!User!

Subscription!

21!

!

Background •  Top investment bank with $1+ trillion in assets •  Using a relational database and Gemfire to manage

employee permissions to research document and application-service resources

•  Permissions for new investment managers and traders provisioned manually

Business Problem •  Lost an average of 5 days per new hire while they

waited to be granted access to hundreds of resources, each with its own permissions

•  Replace an unsuccessful onboarding process implemented by a competitor

•  Regulations left no room for error

Solution and Benefits •  Store models, groups and entitlements in Neo4j •  Exceeded performance requirements •  Major productivity advantage due to domain fit •  Graph visualization ease permissioning process •  Fewer compromises than with relational •  Expanded Neo4j solution to online brokerage

London Investment Bank FINANCIAL SERVICES

Identity and Access Management!22!

!

Background •  Global financial services firm with trillions of

dollars in assets •  Varying compliance and governance

considerations •  Incredibly complex transaction systems, with ever-

growing opportunities for fraud

Business Problem •  Needed to spot and prevent fraud detection in real

time, especially in payments that fall within “normal” behavior metrics

•  Needed more accurate and faster credit risk analysis for payment transactions

•  Needed to dramatically reduce chargebacks

Solution and Benefits •  Lowered TCO by simplifying credit risk analysis and

fraud detection processes •  Identify entities and connections uniquely •  Saved billions by reducing chargebacks and fraud •  Enabled building real-time apps with non-uniform

data and no sparse tables or schema changes

London and New York Financial FINANCIAL SERVICES

Fraud Detection!

s!

23!

!

Background •  Panama based lawyers Mossack & Fonseca do

business in hosting “letterbox companies” •  Suspected to support tax saving and organized

crime •  Altogether: 2.6 TB, 11 milo files, 214.000 letter box

companies

Business Problem •  Goal to unravel chains Bank-Person–Client–

Address–Intermediaries – M&F •  Earlier cases: spreadsheet based analysis (back-

and-forth) & pencil to extract such connections •  This case: sheer amount of data & arbitrarily chain

length condemn such approaches to fail

Solution and Benefits •  400 journalists, investigate/update/share, 2 people

with IT background •  Identify connections quickly and easily •  Fast Results wouldn‘t be possible without GraphDB

Panama Papers Fraud Detection

Fraud Detection!24!

MDM Status Quo – viele kleine Königreiche

Dr. Andreas Weber | semantic data management | 11.11.2016 !

QS / LIMS

ERP

Logistik Warehouse- management

Produkt-management

Technisches PDM/PLM

Dokumenten- management

Excel

Excel Power-point

Power-point

Excel

Excel

!

Adidas Meta Data Management

27! Shared Meta Data Service!

Background •  Global leader in sporting goods industry services

firm footware, apparel, hardware, 14.5 bln sales, 53,000 people

•  Multitude of products, markets, media, assets and audiences

Business Problem •  Beset by a wide array of information silos including

data about products, markets, social media, master data, digital assets, brand content and more

•  Provide the most compelling and relevant content to consumers

•  Offering enhanced recommendations to drive revenue

Solution and Benefits •  Save time and cost through stadardized access to

content sharing-system with internal teams, partners, IT units, fast, reliable, searchable avoiding reduandancy

•  Inprove customer experience and increase revenue by providing relevant content and recommentations

!

Background •  Mid-size German insurer founded in 1858 •  Project executed by Delvin, a subsidiary

of die Bayerische Versicherung and an IT insurance specialist

Business Problem •  Field sales needed easy, dynamic, 24/7 access to

policies and customer data •  Existing DB2 system unable to meet performance

and scaling demands

Solution and Benefits •  Enabled flexible searching of policies and associated

personal data •  Raised the bar on industry practices •  Delivered high performance and scalability •  Ported existing metadata easily

Die Bayerische INSURANCE

Master Data Management!28!

!

Background •  Leading European Airline •  100+ mln passengers •  2+ mln tons freight per year •  700+ aircrafts

Business Problem •  Need for flexible high performant Inflight Asset

Management, onboard entertainment, byod •  Complex data set: CMDB, CMS, Aircraft data feed,

media library •  Maintain individual configuration for each Aircraft •  Complex data model, aircrafts, hardware, vitual

containers, licenses, business rules, versions, content ...

Solution and Benefits •  Neo4j powers integrated platform that provides fast

access to all aspects needed to maintain complex system

•  Fast implementation •  Higly flexible data model enable constant evolution

Lufthansa Digital Asset Mangagement

29! Graph Based Search, Metadata Management!

!

Background •  Toy Manufacturer, founded 80+ years ago, plastic

figurines sold in 50+ countries •  100 Mio, 250 employees •  Production Process in different countries like China •  Polymer Processing, Children‘s toys, high

responsibility

Business Problem •  Product related data stored in many different data

stores including SAP, Navision, Laboratory Systems, Document Systems, Powerpoint, Excel..

•  Hard to find correct answers for authorities, , internally, parents

Solution and Benefits •  Neo4j powers integrated platform that provides

visibility across whole supply chain •  Domain Experts create and evolve data model •  Correct answers within seconds

Schleich Product Information Management

30! Master Data Management!

ADAMA!