-
1.
“Graphs in the Real World”
Developed, deployed and
battle-tested graph use-cases
-
2.
Value from Data Relationships
Common 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
-
3.
Graphs for Master Data
Management
-
4.
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
-
5.
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
Fiber
Link
Fiber
Link
Fiber
Link
Ocean
Cable
Switch Switch
Router Router
Service
Organizational
Hierarchy
Product
Hierarchy
Network
Topology
/ CMDB
Social
Network
-
6.
die Bayerische – Master Data Management
Mid-size
German insurer
Founded in 1858
More than
500 employees
Project executed
by Delvin GmbH,
subsidiary of
die Bayerische
Versicherung
360° View of the Customer
-
7.
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
-
8.
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
-
9.
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
-
10.
Source:
“Growing the Elephant: Tales from
an Enterprise Data Model”
by Jeremy Posner (Synechron)
Enterprise Data World 2015
-
11.
Graphs for Network and IT
Operations Management
-
12.
Graphs in Networking
-
13.
The Royal Netherlands
Meteorological Institute
Operational Infrastructure to Collect, Record, and Manage Weather Data
-
14.
Graph Applied to Fraud Detection
-
15.
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
-
16.
Pros
Simple
Stops rookies
Discrete Data Analysis
Revolving
Debt
INVESTIGATE
INVESTIGATE
Number of accounts
Cons
False positives
False negatives
-
17.
Connected Analysis
Revolving
Debt
Number of accounts
PROS
Detect fraud rings
Fewer false negatives
-
18.
Graph of First Party Bank Fraud
Account
Holder
1
Account
Holder
2
Account
Holder
3
SSN
2
SSN
2
Phone
Numbe
r
2
Credit
Card
Address
1
Bank
Account
Bank
Account
Bank
Account
Phone
Numbe
r
2
Credit
Card
Unsecured
Loan
Unsecured
Loan
-
19.
Insurance Fraud Example
-
20.
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 of
navigation
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
-
21.
Graphs for Real-time
Recommendations
-
22.
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
-
23.
Retail Recommendations
-
24.
“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
-
25.
eBay – Real-time routing recommendations
• Order from local stores
• Deliveries within 90 minutes
• Leverage local courier
services
• Calculate best route in real-
time
-
26.
Graphs for Graph-Based Search
-
27.
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”
-
28.
Curaspan WHERE ARE THE GRAPHS?
• Permissions: Caregivers to Patient Data
• Coverage: Organizational Relationships
• Provider Services & Skills
• Service Areas: Location Graph
-
29.
Graphs for Identity and Access
Management
-
30.
Identity & Access Management
• Based in Oslo
• #1 in Nordics
• #10 in world
-
31.
Oslo-based Telco
#1 in Nordic countries
#10 in world
Mission-critical system
Availability and
responsiveness critical to
customer satisfaction
Telenor – Identity & Access Management
-
32.
Source:
Using Graph Databases in
Real-Time to Solve Resource
Authorization at Telenor -
Sebastian Verheughe @
GraphConnect London 2013
-
33.
Value from Data Relationships
Common 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
-
34.
Graphs in the Real World
March 2015
Field sales unit needed easy access to policies and customer data in variety of ways
Growing business needed growing support
Existing IBM DB2 system unable to meet performance requirements as it scaled
Needed 24/7 system for sales unit outside the company
Scale: Neo4j can handle 34B nodes and 34B relationships
Fraudsters have gotten smart in order to pull off large scam or theft, they coordinate multiple bits of activity within shaded area.
Ten people collude to commit insurance fraud, five false accidents are staged
Assuming an average claim of $40K per injured person and $5K per car, the ring can claim up to $1.6M for 40 people injured! where each person plays the role of the driver once, a witness once and a passenger three times.
Need to include all approaches to catch rookies and experienced fraudsters
Can do one or both but able to do more: jump up category trees, etc.
Slowest query on MySQL took longer than their fastest delivery
Discharge nurses and intake coordinators:
Met fast, real-time performance demands
Supported queries span multiple hierarchies including provider and employee-permissions graphs
Improved data model to handle adding more dimensions to the data such as insurance networks, service areas and care organizations
Greatly simplified queries, simplifying multi-page SQL statements into one Neo4j function
Improve poor performance of Oracle solution
Support more complexity including granular, role-based access control
Different roles use the tool and different roles able to see different things
Need a smart search – not just searching for a keyword – data model according to natural structure and then exposing for search gives you enormous power when searching