Transforming Your Data: A Worked Example
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This talk examines graph databases and Neo4j with a use-case driven approach. First, we look at some property graph model examples, taken from real-world datasets. Next we discuss converting a relational model to graph, using the canonical Northwind example. Finally, we dive into Fraud Detection and Personalized Recommendation examples, learning about Neo4j developer tooling as we explore these use cases.
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Transforming Your Data: A Worked Example
- 1. 09:00-09:30 09:30-10:15 10:15-11:00 11:00-11:30 11:30-12:30 12:30-13:30 13:30-17:00 Breakfast and Registration The Connected Data Imperative: Why Graphs Transform Your Data: A Worked Example Break Enterprise Ready: A Look at Neo4j in Production Lunch Hands-On Training Session Agenda APRIL 26, 2017 SANTA CLARA
- 2. Transform Your Data Neo4j Graph Day - Santa Clara 2017 William Lyon @lyonwj A Worked Example
- 3. William Lyon Developer Relations Engineer @neo4j will@neo4j.com @lyonwj lyonwj.com
- 4. Agenda • Graph database - developer perspective • (relational)-[:TO]->(graph) • Use cases • Fraud Detection • Recommendations • Developer tooling
- 5. Neo4j Graph Database • Property graph data model • (open)Cypher query language • Native graph processing • Language drivers • Open source neo4j.com
- 7. Labeled Property Graph Model
- 8. Labeled Property Graph Model
- 16. Cypher
- 19. Organized in groups Synthetic Identities Stolen Identities Hijacked Devices Who Are Today’s Fraudsters?
- 20. Fraud Rings and Synthetic Identities
- 21. ACCOUNT HOLDER 2 Modeling a fraud ring as a graph ACCOUNT HOLDER 1 ACCOUNT HOLDER 3
- 22. ACCOUNT HOLDER 2 ACCOUNT HOLDER 1 ACCOUNT HOLDER 3 CREDIT CARD BANK ACCOUNT BANK ACCOUNT BANK ACCOUNT PHONE NUMBER UNSECURED LOAN SSN 2 UNSECURED LOAN Modeling a fraud ring as a graph
- 23. ACCOUNT HOLDER 2 ACCOUNT HOLDER 1 ACCOUNT HOLDER 3 CREDIT CARD BANK ACCOUNT BANK ACCOUNT BANK ACCOUNT ADDRESS PHONE NUMBER PHONE NUMBER SSN 2 UNSECURED LOAN SSN 2 UNSECURED LOAN Modeling a fraud ring as a graph
- 30. FROM RDBMS TO GRAPHS
- 32. Northwind
- 33. Northwind - the canonical RDBMS Example
- 37. Starting with the ER Diagram
- 38. Locate the Foreign Keys
- 39. Drop the Foreign Keys
- 40. Find the JOIN Tables
- 41. (Simple) JOIN Tables Become Relationships
- 42. Attributed JOIN Tables -> Relationships with Properties
- 43. As a Graph
- 45. Northwind
- 46. Northwind
- 56. POWERING AN APP
- 57. Simple App
- 58. Simple App
- 65. Recommendations drive user engagement
- 66. “35 percent of what consumers purchase on Amazon and 75 percent of what they watch on Netflix come from product recommendations” http://www.mckinsey.com/industries/retail/our-insights/how-retailers-can-keep-up-with-consumers
- 67. Product Recommendations Effective product recommendation algorithms has become the new standard in online retail — directly affecting revenue streams and the shopping experience. Logistics/Delivery Routing recommendations allows companies to save money on routing and delivery, and provide better and faster service. Promotion recommendations Building powerful personalized promotion engines is another area within retail that requires input from multiple data sources, and real-time, session based queries, which is an ideal task to solve with Neo4j. Today Recommendation Engines are At the Core of Digitization in Retail
- 68. Powerful recommendation engines rely on the connections between multiple sources of data
- 69. Powerful recommendation engines rely on the connections between multiple sources of data
- 70. Dreamhouse Series 15% off The Store Search Hi, login My Account People who bought Side Table also bought: Coffee Table $235 Low Book Shelf $150 Bed Side Table $90 Mobile Brick & Mortar Multi-Channel Web The Store People who bought Side Table also bought: Similar product in from Home Office Series: Hi, login My AccountSearch Dreamhouse Series 15% off All departments Living room | Kitchen | Hallway | Lightning | Bedroom | Garden | Home Office Space Track Orders | Gift Cards | Store finder | Credit Card | Grocery Pickup | Help Wood Side Table $110 Green Side Table $135 Walnut Side Table $120 Coffee Table $235 Low Book Shelf $150 Bed Side Table $90 Product Recommendations
- 71. Dreamhouse Series 15% off The Store Search Hi, login My Account People who bought Side Table also bought: Coffee Table $235 Low Book Shelf $150 Bed Side Table $90 Mobile Brick & Mortar Web The Store People who bought Side Table also bought: Similar product in from Home Office Series: Hi, login My AccountSearch Dreamhouse Series 15% off All departments Living room | Kitchen | Hallway | Lightning | Bedroom | Garden | Home Office Space Track Orders | Gift Cards | Store finder | Credit Card | Grocery Pickup | Help Wood Side Table $110 Green Side Table $135 Walnut Side Table $120 Coffee Table $235 Low Book Shelf $150 Bed Side Table $90
- 72. The Store People who bought Side Table also bought: Similar product in from Home Office Series: Hi, login My AccountSearch Dreamhouse Series 15% off All departments Living room | Kitchen | Hallway | Lightning | Bedroom | Garden | Home Office Space Tra c k O rd e r s | G i f t C a rd s | S t o re fi n d e r | C re d i t C a rd | G ro c e r y P i c k u p | H e lp Wood Side Table $110 Green Side Table $135 Walnut Side Table $120 Coffee Table $235 Low Book Shelf $150 Bed Side Table $90 Personalized Promotions Personalized Real-Time Recommendations Personalized Real-Time Recommendations
- 73. People who bought Side Table also bought: Similar product in from Home Office Series: Wood Side Table $110 Green Side Table $135 Walnut Side Table $120 Coffee Table $235 Low Book Shelf $150 Bed Side Table $90 Data-Model (Expressed as a graph) Category Category Product Product Product Collaborative Filtering An algorithm that considers users interactions with products, with the assumption that other users will behave in similar ways. Algorithm Types Content Based An algorithm that considers similarities between products and categories of products. Customer Customer Product Product Product
- 74. Category Price ConfigurationsLocation Silos & Polyglot Persistence Purchase ViewReviewReturn In-store PurchasesInventory Products Customers / Users Location Purchases RELATIONAL DB WIDE COLUMN STORE Views DOCUMENT STORE User Review RELATIONAL DB In-Store Purchase Shopping Cart KEY VALUE STORE Product Catalogue DOCUMENT STORE
- 75. Purchases RELATIONAL DB WIDE COLUMN STORE Views DOCUMENT STORE User Review RELATIONAL DB In-Store Purchase Shopping Cart KEY VALUE STORE Product Catalogue DOCUMENT STORE Silos & Polyglot Persistence Category Price ConfigurationsLocation Purchase ViewReviewReturn In-store PurchasesInventory Products Customers / Users Location
- 76. Purchases RELATIONAL DB WIDE COLUMN STORE Views DOCUMENT STORE User Review RELATIONAL DB In-Store Purchase Shopping Cart KEY VALUE STORE Product Catalogue DOCUMENT STORE Category Price ConfigurationsLocation Polyglot Persistence Purchase ViewReviewReturn In-store PurchasesInventory LocationCategory Price ConfigurationsLocation Purchase ViewReviewReturn In-store PurchasesInventory Products Customers / Users Location
- 77. Data Lake Purchases RELATIONAL DB Product Catalogue DOCUMENT STORE WIDE COLUMN STORE Views DOCUMENT STORE User Review RELATIONAL DB In-Store Purchase Shopping Cart KEY VALUE STORE Recommendations require an operational workload — it’s in the moment, real-time! Good for Analytics, BI, Map Reduce Non-Operational, Slow Queries
- 78. Purchases RELATIONAL DB Product Catalogue DOCUMENT STORE WIDE COLUMN STORE Views DOCUMENT STORE User Review RELATIONAL DB In-Store Purchase Shopping Cart KEY VALUE STORE Connector Drivers: Java | JavaScript | Python | .Net | PHP | Go | Ruby Apps and Systems Real-Time Queries
- 79. Using Data Relationships for Recommendations Content-based filtering Recommend items based on what users have liked in the past Collaborative filtering Predict what users like based on the similarity of their behaviors, activities and preferences to others Movie Person Person RATED SIMILARITY rating: 7 value: .92
- 84. In Cypher
- 85. In Cypher Basic initial approach. Improvements: - aggregate across all purchases - scoring / normalize - compute similarity metrics
- 88. Content Filtering w/ Cypher
- 89. Content Filtering - Concept Hierarchy
- 90. Content Filtering - Concept Hierarchy
- 91. Content Filtering - Concept Hierarchy
- 92. Content Filtering - Concept Hierarchy w/ Cypher
- 93. Content Filtering - Concept Hierarchy w/ Cypher Basic initial approach. Improvements: - aggregate across all purchases - cold start - variable length concept hierarchy - tag similarity / clusters
- 95. Want to learn more? graphdatabases.com neo4j.com/sandbox
- 97. 09:00-09:30 09:30-10:15 10:15-11:00 11:00-11:30 11:30-12:30 12:30-13:30 13:30-17:00 Breakfast and Registration The Connected Data Imperative: Why Graphs Transform Your Data: A Worked Example Break Enterprise Ready: A Look at Neo4j in Production Lunch Hands-On Training Session Agenda APRIL 26, 2017 SANTA CLARA