Post on 14-Jul-2015
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
MDM Solutions with Graph Databases
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 Subscriptions
CMDBNetworkInventory
Social Networks
MDM Isn’t Hierarchical
Typical MDM system structure …but MDM is really a network
Patient
Agent
G.P.Surgeon Partner
Insurance
Patient
AgentG.P.Surgeon
PartnerInsurance
Challenges with Current MDM Systems
Lack of support for non-hierarchical or matrix data relationships
• Master data is never strictly hierarchical
• Systems are designed for fixed top-down hierarchy
• Non-hierarchical data is not supported
Inability to unlock value from data relationships
• Systems store only very simple data relationships
• Complex relationships and links not stored
Inflexible and expensive to maintain
• Changes to the model are expensive and time-consuming
die Bayerische – Master Data Management
• 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
Mid-size German insurer
Founded in 1858
More than 500 employees
Project executed by Delvin GmbH,
subsidiary of die BayerischeVersicherung
die Bayerische SOLUTION
• Enables field sales unit to flexibly search for insurance policies and personal data
• Raises the bar for insurance industry practices
• Supports the business as it scales, with great performance
• Ported metadata into Neo4j easily
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
Network Graphs – Telco Example
PROBLEM
Need: Instantly diagnose problems in networks of 1B+ elements
But: Basing diagnosis solely on streaming machine data severely limits accuracy and effectiveness
SOLUTION
Real-time graph analytics provide actionable insight for the largest complex connected networks in the world
• The entire network lives in a graph
• Analyzes dependencies in real time
• Highly scalable with carrier-grade uptime requirements
Fraud Scenarios
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
Doing Connected Analysis is Challenging
• Large amounts of data and relationships must be processed
• New data and relationships are continually being added
• Fraud rings must be uncovered in real-time to prevent fraud
ValueEffective in detecting some of the most impactful attacks, even from organized rings
ChallengeExtremely difficult with traditional technologies
For example a ten-person fraud bust-out is $1.5M, assuming 100 false identities and 3 financial instruments per identity, each with a $5K credit limit
Connected Analysis with Neo4j
Modeling a Fraud Ring as a Graph
AccountHolder
1
AccountHolder
2
AccountHolder
3
SSN2
SSN2
PhoneNumbe
r2
CreditCard
Address1
BankAccount
BankAccount
BankAccount
PhoneNumbe
r2
CreditCard
UnsecuredLoan
UnsecuredLoan
View of fraud ring in a graph database
Modeling Insurance Fraud as a Graph
Accident1
Accident2
Person1
Person2
Person3
Person4
Person5
Person6
Car1
Car2
Car3
Car4
INVOLVES
DRIVES
REPRESENTS
WITNESSES
ADJUSTS
HEALS
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
Real-Time Recommendations - Benefits
Online Retail• Suggest related products and services• Increase revenue and engagement
Media and Broadcasting• Create an engaging experience• Produce personalized content and offers
Logistics• Recommend optimal routes• Increase network efficiency
Real-Time Recommendations - Challenges
Make effective real-time recommendations
• Timing is everything in point-of-touch applications
• Base recommendations on current data, not last night’s batch load
Process large amounts of data and relationships for context
• Relevance is king: Make the right connections
• Drive traffic: Get users to do more with your application
Accommodate new data and relationships continuously
• Systems get richer with new data and relationships
• Recommendations become more relevant
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
Walmart – Retail Recommendations
World’s largest companyby revenue
World’s largest retailer and private employer
SF-based global e-commerce division
manages several websites
Found in 1969Bentonville, Arkansas
• Needed online customer recommendations to keep pace with competition
• Data connections provided predictive context, but were not in a usable format
• Solution had to serve many millions of customers and products while maintaining superior scalability and performance
Walmart SOLUTION
• Brings customers, preferences, purchases, products and locations into a graph model
• Uses data relationships to make product recommendations
• Solution deployed across Walmartdivisions and websites
N eo Tec h n o l o g y , I n c C o n f i d en t i a l
GRAPHS ARE EATING RETAIL
CUSTOMERS ORDERS PRODUCT
CATEGORY
THE PROBLEM
CONNECTIONS HOLD PREDICTIVE CONTEXT
CONNECTIONS IN THE DATA NOT IN A
USABLE FORMAT
OTHER EXAMPLES
THE SOLUTION
BRING THE DATA INTO A GRAPH
SO THAT THE CONNECTIONS
CAN BE USED TO MAKE
PRODUCT RECOMMENDATIONS.
COMPETITIVE PRESSURE DEMANDS ONLINE
RECOMMENDATIONS.
eBay – Real-time routing recommendations
C2C and B2Cretail network
Full e-commerce functionality for individuals and
businesses
Integrated with logistics vendors for product
deliveries
• Needed an offering to compete with Amazon Prime and Google Express
• Enable customer-selected delivery inside 90 minutes
• Calculate best route option in real-time
• Scale to enable a variety of services
• Offer more predictable delivery times
eBay Now SOLUTION
• Acquired UK-based Shutl, a leader in same-day delivery
• Used Neo4j to create eBay Now
• 1000 times faster than the prior MySQL-based solution
• Faster time-to-market
• Improved code quality with 10 to 100 times less query code
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
• Improve poor performance of Oracle solution
• Support more complexity including granular, role-based access control
• Satisfy complex Graph Search queries by discharge nurses and intake coordinatorsFind a skilled nursing facility within n miles of a given location, belonging to health care group XYZ, offering speech therapy and cardiac care, and optionally Italian language services
Curaspan SOLUTION
• 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
Telenor – Identity & Access Management
Oslo-based Telco#1 in Nordic countries
#10 in world
Mission-critical system
Availability and responsiveness critical to
customer satisfaction
Millions of plans, customers, admins, groups • Highly interconnected data set with massive joins
Degrading relational performance• Login took minutes to retrieve access rights
Nightly batch workaround• Solved performance problem, but meant data was
not current
Replace slow Sybase system• Batch workaround reached 9 hours in 2014—longer
than the nightly batch window
Telenor SOLUTION
• Modeling resource graph was straightforward, as the domain is a graph
• Moved authorization from Sybase to Neo4j
• Retired faulty nightly batch process
• Moved real-time response to milliseconds
• Showed fresh data, not yesterday’s snapshot
• Addressed customer retention risks
• Kept business running through aggressive data growth
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