“Graphs in the Real World” Developed, deployed and battle-tested graph use-cases
Jul 16, 2015
“Graphs in the Real World”
Developed, deployed and battle-tested graph use-cases
Value from Data RelationshipsCommon Graph Database Use Cases
Internal Applications
Master Data Management
Network and IT Operations
Fraud Detection
Customer-Facing Applications
Real-Time Recommendations
Graph-Based Search
Identity and Access Management
Graphs for Master Data Management
MDM as a Graph
What we *think* MDM is What MDM *really* is
Patient
Agent
G.P.Surgeon Partner
Insurance
Patient
AgentG.P.Surgeon
PartnerInsurance
Common Graphs in Master Data Management
C
C
A AA
U
S S SS S
USER_ACCESS
CONTROLLED_BY
SUBSCRIBED _BY
User
Customers
Accounts
Subscriptions
VP
Staff Staff StaffStaff
DirectorStaffDirector
Manager Manager Manager Manager
FiberLink
FiberLink
FiberLink
Ocean Cable
Switch Switch
Router Router
Service
OrganizationalHierarchy
Product Hierarchy
Network Topology/ CMDB
Social Network
die Bayerische – Master Data Management
Mid-size German insurer
Founded in 1858
More than 500 employees
Project executed by Delvin GmbH,
subsidiary of die BayerischeVersicherung
360° View of the Customer
die Bayerische SOLUTION
• Complete view customer & policy information by Field Sales
• Flexibly policy & customer search
• Overcome scaling limitations of existing IBM DB2 system
• Extend information to sales partners
Classmates – Social network
Online yearbook connecting friends from
school, work and military in US and Canada
Founded as Memory Lane in Seattle
Develop new social networking capabilities to monetize yearbook-related offerings
• Show all the people I know in a yearbook
• Show yearbooks my friends appear in most often
• Show sections of a yearbook that my friends appear most in
• Show me other schools my friends attended
Classmates SOLUTION
Neo4j provides a robust and scalable graph database solution
• 3-instance cluster with cache sharding and disaster-recovery
• 18ms response time for top 4 queries
• 100M nodes and 600M relationships in initial graph—including people, images, schools, yearbooks and pages
• Projected to grow to 1B nodes and 6B relationships
Source:“Growing the Elephant: Tales from an Enterprise Data Model”by Jeremy Posner (Synechron)Enterprise Data World 2015
Graphs for Network and IT Operations Management
Graphs in Networking
The Royal Netherlands Meteorological Institute
Operational Infrastructure to Collect, Record, and Manage Weather Data
Graph Applied to Fraud Detection
Some Examples
Retail First Party Fraud• Opening many lines of credit with no intention of paying back
• Accounts for $10B+ in annual losses at US banks(1)
Synthetic Identities and Fraud Rings• Rings of synthetic identities committing fraud
Insurance – Whiplash for Cash• Insurance scams using fake drivers, passengers and witnesses• Increase network efficiency
eCommerce Fraud• Online payment fraud
(1) Business Insider: http://www.businessinsider.com/how-to-use-social-networks-in-the-fight-against-first-party-fraud-2011-3
ProsSimpleStops rookies
Discrete Data Analysis
RevolvingDebt
INVESTIGATE
INVESTIGATE
Number of accounts
ConsFalse positivesFalse negatives
Connected Analysis
RevolvingDebt
Number of accounts
PROSDetect fraud rings
Fewer false negatives
Graph of First Party Bank Fraud
AccountHolder
1
AccountHolder
2
AccountHolder
3
SSN2
SSN2
PhoneNumbe
r2
CreditCard
Address1
BankAccount
BankAccount
BankAccount
PhoneNumbe
r2
CreditCard
UnsecuredLoan
UnsecuredLoan
Insurance Fraud Example
Gartner’s Layered Fraud Prevention Approach (4)
(4) http://www.gartner.com/newsroom/id/1695014
Traditional Fraud Prevention
Analysis of users
and their endpoints
Analysis ofnavigation
behavior and suspect patterns
Analysis of anomaly
behavior by channel
Analysis of anomaly behavior
correlated across channels
Analysis of relationships
to detect organized crime
and collusion
Layer 1
Endpoint-Centric
Navigation-Centric
Account-Centric
Cross-Channel
Entity Linking
Layer 2 Layer 3 Layer 4 Layer 5
DISCRETE DATA ANALYSIS CONNECTED ANALYSIS
Graphs for Real-time Recommendations
Using Data Relationships for Recommendations
Collaborative filtering
Predict what users like based on the similarity of their behaviors, activities and preferences to others
Content-based filtering
Recommend items based on what users have liked in the past
Movie
Person
Person
Retail Recommendations
“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, eBay
eBay – Real-time routing recommendations
• Order from local stores
• Deliveries within 90 minutes
• Leverage local courier services
• Calculate best route in real-time
Graphs for Graph-Based Search
Curaspan – Graph-based Search
Leader in patient management for
discharges and referrals
Manages patient referrals 4600+ health care facilities
Connects providers, payers via web-based patient management platform
Founded in 1999 in Newton, Massachusetts
“Find a skilled nursing facility within 5 miles of the patient’s home, belonging to an eligible health care group, offering speech therapy and cardiac care, and optionally Italian language services”
Curaspan WHERE ARE THE GRAPHS?
• Permissions: Caregivers to Patient Data
• Coverage: Organizational Relationships
• Provider Services & Skills
• Service Areas: Location Graph
Graphs for Identity and Access Management
Identity & Access Management
• Based in Oslo
• #1 in Nordics
• #10 in world
Oslo-based Telco#1 in Nordic countries
#10 in world
Mission-critical system
Availability and responsiveness critical to
customer satisfaction
Telenor – Identity & Access Management
Source:
Using Graph Databases in Real-Time to Solve Resource
Authorization at Telenor -Sebastian Verheughe @
GraphConnect London 2013
Value from Data RelationshipsCommon Graph Database Use Cases
Internal Applications
Master Data Management
Network and IT Operations
Fraud Detection
Customer-Facing Applications
Real-Time Recommendations
Graph-Based Search
Identity and Access Management
Graphs in the Real World
March 2015