Graphs in the Real World
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Graphs in the Real World
- 1. Graphs in the Real World March 2015
- 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 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. 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. 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. 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. 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. 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. 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. Graphs for Network and IT Operations Management
- 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. Graphs for Fraud Detection
- 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. Pros Simple Stops rookies Discrete Data Analysis Revolving Debt INVESTIGATE INVESTIGATE Number of accounts Cons False positives False negatives
- 16. Connected Analysis Revolving Debt Number of accounts PROS Detect fraud rings Fewer false negatives
- 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. 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. 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. 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. 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
- 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. 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. 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. 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. 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. 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. 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. 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 • 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. 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. Graphs for Identity and Access Management
- 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. 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. 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. Graphs in the Real World March 2015
Notas del editor
- Scale: Neo4j can handle 34B nodes and 34B relationships
- Top Uses: Impact Analysis (e.g. Servers to Services to Users) Root Cause Analysis Network Design Network Security Analysis Top Queries: Trace dependencies up from servers all the way to applications and users Trace dependencies across virtual and physical layers of infrastructure Identify routes & alternate paths between various points in the network Find the best, shortest, or least busy path, the best location in the network to introduce a new service
- Fraudsters have gotten smart in order to pull off large scam or theft, they coordinate multiple bits of activity within shaded area.
- The kind of analysis that needs to be done Challenges: very difficult to model and carry out, and even then can be done only after the fact almost impossible in real-time
- Beyond this example, many other ways to detect fraud. By understanding the user across multiple channels of business, able to avoid being gamed by the customer.
- 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.
- Valuable predictive information if able to understand what people bought but making prediction of what they are likely to buy required adopting a graph database – data was in tables and unable to perform rich queries for recommendations
- Slowest query on MySQL took longer than their fastest delivery
- “We run our business on 7 lines of Cypher” – Volker
- 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