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

6. Graph Data Model

7. Labeled Property Graph Model

8. Labeled Property Graph Model

9. The Graph https://github.com/johnymontana/neo4j-datasets/tree/master/yelp

10. The Graph https://www.youtube.com/watch?v=kSZHFlBDIfM

11. The Graph https://github.com/johnymontana/mattermark-graphql-neo4j

12. The Graph https://github.com/neo4j-meetups/modeling-worked-example

13. The Graph https://neo4j.com/sandbox-v2/

14. openCypher (queryLanguage)-[:FOR]->(graphs) http://www.opencypher.org/

15. Cypher

16. https://neo4j.com/sandbox-v2/

17. Use Case: Fraud Detection

18. Organized in groups Synthetic Identities Stolen Identities Hijacked Devices Who Are Today’s Fraudsters?

19. Fraud Rings and Synthetic Identities

20. ACCOUNT HOLDER 2 Modeling a fraud ring as a graph ACCOUNT HOLDER 1 ACCOUNT HOLDER 3

21. 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

22. 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

23. https://neo4j.com/graphgist/9d627127-003b-411a-b3ce-f8d3970c2afa?ref=solutions https://neo4j.com/graphgists/

24. Credit Card Fraud

25. (relational)-[:TO]->(graph)

26. FROM RDBMS TO GRAPHS

27. Northwind

28. Northwind - the canonical RDBMS Example

29. ( )-[:TO]->(Graph)

30. ( )-[:IS_BETTER_AS]->(Graph)

31. Starting with the ER Diagram

32. Locate the Foreign Keys

33. Drop the Foreign Keys

34. Find the JOIN Tables

35. (Simple) JOIN Tables Become Relationships

36. Attributed JOIN Tables -> Relationships with Properties

37. As a Graph

38. https://github.com/neo4j-contrib/neo4j-apoc-procedures/blob/master/readme.adoc

39. Northwind

40. Northwind

41. Neo4j Language Drivers

42. Neo4j Drivers https://neo4j.com/developer/language-guides/

43. POWERING AN APP

44. Simple App

45. Simple App

46. Simple Python Code

50. https://neo4j.com/developer/

51. Use Case: Personalized Recommendations

52. Recommendations drive user engagement

53. “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

54. 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

55. Powerful recommendation engines rely on the connections between multiple sources of data

56. Powerful recommendation engines rely on the connections between multiple sources of data

57. 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

58. 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

59. 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

60. 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

61. 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

62. 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

63. 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

64. 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

65. 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

66. 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

67. Collaborative Filtering

70. Collaborative Filtering Link prediction

71. In Cypher

72. In Cypher Basic initial approach. Improvements: - aggregate across all purchases - scoring / normalize - compute similarity metrics

73. Content Filtering

74. Content Filtering

75. Content Filtering w/ Cypher

76. Content Filtering - Concept Hierarchy

79. Content Filtering - Concept Hierarchy w/ Cypher

80. Content Filtering - Concept Hierarchy w/ Cypher Basic initial approach. Improvements: - aggregate across all purchases - cold start - variable length concept hierarchy - tag similarity / clusters

81. Neo4j Sandbox neo4jsandbox.com

82. Want to learn more? graphdatabases.com neo4j.com/sandbox

83. (you)-[:HAVE]->(?) (?)<-[:ANSWERS]-(will)

84. 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