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Graphs in the Real World
March 2015
Value from Data Relationships
Common Graph Database Use Cases
Internal Applications
Master Data Management
Network and
IT ...
Graphs for Master Data
Management
MDM Solutions with Graph Databases
C
C
A AA
U
S S SS S
USER_ACCESS
CONTROLLED_BY
SUBSCRIBED _BY
User
Customers
Accounts
Su...
MDM Isn’t Hierarchical
Typical MDM system structure …but MDM is really a network
Patient
Agent
G.P.Surgeon Partner
Insuran...
Challenges with Current MDM Systems
Lack of support for non-hierarchical or matrix data relationships
• Master data is nev...
die Bayerische – Master Data Management
• Field sales unit needed easy access to policies
and customer data in variety of ...
die Bayerische SOLUTION
• Enables field sales unit to flexibly search
for insurance policies and personal data
• Raises th...
Classmates – Social network
Online yearbook
connecting friends from
school, work and military
in US and Canada
Founded as
...
Classmates SOLUTION
Neo4j provides a robust and scalable graph
database solution
• 3-instance cluster with cache sharding
...
Graphs for Network and IT
Operations Management
Network Graphs – Telco Example
PROBLEM
Need: Instantly diagnose problems in networks of 1B+ elements
But: Basing diagnosis...
Graphs for Fraud Detection
Fraud Scenarios
Retail First Party Fraud
• Opening many lines of credit with no intention of paying back
• Accounts for $1...
Pros
Simple
Stops rookies
Discrete Data Analysis
Revolving
Debt
INVESTIGATE
INVESTIGATE
Number of accounts
Cons
False posi...
Connected Analysis
Revolving
Debt
Number of accounts
PROS
Detect fraud rings
Fewer false negatives
Doing Connected Analysis is Challenging
• Large amounts of data and relationships
must be processed
• New data and relatio...
Value
Effective in detecting some of the
most impactful attacks, even from
organized rings
Challenge
Extremely difficult w...
Modeling a Fraud Ring as a Graph
Account
Holder
1
Account
Holder
2
Account
Holder
3
SSN
2
SSN
2
Phone
Numbe
r
2
Credit
Car...
View of fraud ring
in a graph database
Modeling Insurance Fraud as a Graph
Accident
1
Accident
2
Person
1
Person
2
Person
...
Gartner’s Layered Fraud Prevention Approach (4)
(4) http://www.gartner.com/newsroom/id/1695014
Traditional Fraud Preventio...
Graphs for Real-time
Recommendations
Real-Time Recommendations - Benefits
Online Retail
• Suggest related products and services
• Increase revenue and engageme...
Real-Time Recommendations - Challenges
Make effective real-time recommendations
• Timing is everything in point-of-touch a...
Using Data Relationships for Recommendations
Collaborative filtering
Predict what users like based on the
similarity of th...
Walmart – Retail Recommendations
World’s largest company
by revenue
World’s largest retailer
and private employer
SF-based...
Walmart SOLUTION
• Brings customers, preferences, purchases,
products and locations into a graph model
• Uses data relatio...
eBay – Real-time routing recommendations
C2C and B2C
retail network
Full e-commerce
functionality for
individuals and
busi...
eBay Now SOLUTION
• Acquired UK-based Shutl, a leader
in same-day delivery
• Used Neo4j to create eBay Now
• 1000 times fa...
Graphs for Graph-Based Search
Curaspan – Graph-based Search
Leader in patient
management for
discharges and referrals
Manages patient referrals
4600+ he...
Curaspan SOLUTION
• Met fast, real-time performance demands
• Supported queries span multiple hierarchies
including provid...
Graphs for Identity and Access
Management
Telenor – Identity & Access Management
Oslo-based Telco
#1 in Nordic countries
#10 in world
Mission-critical system
Availa...
Telenor SOLUTION
• Modeling resource graph was straightforward, as the domain is a graph
• Moved authorization from Sybase...
Value from Data Relationships
Common Graph Database Use Cases
Internal Applications
Master Data Management
Network and
IT ...
Graphs in the Real World
March 2015
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Graphs in the Real World

Graph Database use cases

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Graphs in the Real World

  1. 1. Graphs in the Real World March 2015
  2. 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. 3. Graphs for Master Data Management
  4. 4. 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 Fiber Link Fiber Link Fiber Link Ocean Cable Switch Switch Router Router Service Organizational Hierarchy Product Subscriptions CMDB Network Inventory Social Networks
  5. 5. 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
  6. 6. 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
  7. 7. 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 Bayerische Versicherung
  8. 8. 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
  9. 9. 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
  10. 10. 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
  11. 11. Graphs for Network and IT Operations Management
  12. 12. 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
  13. 13. Graphs for Fraud Detection
  14. 14. 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
  15. 15. Pros Simple Stops rookies Discrete Data Analysis Revolving Debt INVESTIGATE INVESTIGATE Number of accounts Cons False positives False negatives
  16. 16. Connected Analysis Revolving Debt Number of accounts PROS Detect fraud rings Fewer false negatives
  17. 17. 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
  18. 18. Value Effective in detecting some of the most impactful attacks, even from organized rings Challenge Extremely 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
  19. 19. Modeling a Fraud Ring as a Graph 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
  20. 20. View of fraud ring in a graph database Modeling Insurance Fraud as a Graph Accident 1 Accident 2 Person 1 Person 2 Person 3 Person 4 Person 5 Person 6 Car 1 Car 2 Car 3 Car 4 INVOLVES DRIVES REPRESENTS WITNESSE S ADJUSTS HEALS
  21. 21. 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
  22. 22. Graphs for Real-time Recommendations
  23. 23. 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
  24. 24. 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
  25. 25. 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
  26. 26. Walmart – Retail Recommendations World’s largest company by revenue World’s largest retailer and private employer SF-based global e-commerce division manages several websites Found in 1969 Bentonville, 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
  27. 27. Walmart SOLUTION • Brings customers, preferences, purchases, products and locations into a graph model • Uses data relationships to make product recommendations • Solution deployed across Walmart divisions and websites N eo Tec h n o l o g y, I n c C o n f i d en t i al 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.
  28. 28. eBay – Real-time routing recommendations C2C and B2C retail 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
  29. 29. 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
  30. 30. Graphs for Graph-Based Search
  31. 31. 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 coordinators Find 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
  32. 32. 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
  33. 33. Graphs for Identity and Access Management
  34. 34. 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
  35. 35. 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
  36. 36. 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
  37. 37. Graphs in the Real World March 2015

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  • bunkertor

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Graph Database use cases

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