Neo4j graphs in the real world - graph days d.c. - april 14, 2015
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Graphs in the Real World
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Neo4j graphs in the real world - graph days d.c. - april 14, 2015
- 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
- 15. The Royal Netherlands Meteorological Institute Operational Infrastructure to Collect, Record, and Manage Weather Data
- 16. Graph Applied to Fraud Detection
- 17. 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
- 18. Pros Simple Stops rookies Discrete Data Analysis Revolving Debt INVESTIGATE INVESTIGATE Number of accounts Cons False positives False negatives
- 19. Connected Analysis Revolving Debt Number of accounts PROS Detect fraud rings Fewer false negatives
- 20. 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
- 22. 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
- 24. 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. “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
- 29. eBay – Real-time routing recommendations • Order from local stores • Deliveries within 90 minutes • Leverage local courier services • Calculate best route in real- time
- 30. Graphs for Graph-Based Search
- 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 “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”
- 32. Curaspan WHERE ARE THE GRAPHS? • Permissions: Caregivers to Patient Data • Coverage: Organizational Relationships • Provider Services & Skills • Service Areas: Location Graph
- 33. Graphs for Identity and Access Management
- 34. Identity & Access Management • Based in Oslo • #1 in Nordics • #10 in world
- 35. Oslo-based Telco #1 in Nordic countries #10 in world Mission-critical system Availability and responsiveness critical to customer satisfaction Telenor – Identity & Access Management
- 36. Source: Using Graph Databases in Real-Time to Solve Resource Authorization at Telenor - Sebastian Verheughe @ GraphConnect London 2013
- 39. 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
- 40. Graphs in the Real World March 2015
Notas do Editor
- 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