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GraphTour - Keynote
- 2. Welcome to GraphTour Milan! Neo4j – The Graph Platform Bill Brooks Territory Manager, South Europe bill@neo4j.com
- 3. Neo4j GraphTour Milan Check List
- 4. Neo4j Team
- 5. Italian Speakers and Sponsor Partner
- 6. Neo4j Team Claudia Remlinger, Director of Marketing Alex Erdl, Senior Marketng Manager Stefan Kolmar, VP Field Engineering
- 7. 7 Agenda
- 8. Make connections and Learn
- 10. Graph Tour Milano #1 Database for Connected Data Dirk Möller Director Sales CEMEA dirk@neo4j.com 2/4/19
- 11. Agenda • Impact of Graphs • State of the Graph • Three waves • What‘s enabling all of this? • AI and Graph
- 13. ACCOUNT ADDRESS PERSON PERSON NAME STREET BANK NAME COMPANY BANK BAHAMAS 2.6 TB 11.5 million documents Emails, Scanned Documents, Bank Statements etc…
- 14. 2.6 TB 11.5 million documents Emails, Scanned Documents, Bank Statements etc… Person B Bank US Account 123 Person A Acme Inc Bank Bahama s Address XNODE RELATIONSHIP
- 15. 2.6 TB 11.5 million documents Emails, Scanned Documents, Bank Statements etc…
- 19. ICIJ Pulitzer Price Winner 2017
- 23. State of the graph
- 24. “Forrester estimates that over 25% of enterprises will be using graph databases by 2017.” Forrester, 2014
- 25. Popularity of Graphs DB-engines Ranking of Database Categories • Graph DBMS • Key-value stores • Document stores • Wide column store • RDF stores • Time stores • Native XML DBMS • Object oriented DBMS • Multivalue DBMS • Relational DBMS Graph DB 2013 2014 2015 2016 2017 2018
- 26. Software Financial Services Telecom Retail & Consumer Goods Media & Entertainment Other Industries Airbus Over 300 Enterprises and 10s of Thousands of Projects on Neo4j
- 27. 7 of the Top 10 Software Companies Use Neo4j
- 28. 8 of the Top 10 Insurance Companies Use Neo4j
- 29. Category Defining Use Cases airbnb Fraud Detection Real-Time Recommendations Network & IT Operations Master Data Management Knowledge Graph Identity & Access Management
- 30. 10M+ Downloads 3M+ from Neo4j Distribution 7M+ from Docker Events 400+ Approximate Number of Neo4j Events per Year 50k+ Meetups Number of Meetup Members Globally Largest pool of graph technologists 50k+ Trained/certified Neo4j professionals Trained Developers
- 31. 2012 2018 May 10th-11th, London CONFERENCE + TRAINING
- 33. of enterprises are using graph databases As of today Source: Forrester Vendor Landscape: Graph Databases, October 6, 2017
- 34. "Neo4j continues to dominate the graph database market.” “69% of enterprises have, or are planning to implement graphs over next 12 months” October, 2017 “The most widely stated reason in the survey for selecting Neo4j was to drive innovation” February, 2018 Critical Capabilities for DBMSA “In fact, the rapid rise of Neo4j and other graph technologies may signal that data connectedness is indeed a separate paradigm from the model consolidation happening across the rest of the NoSQL landscape.” March, 2018 Graph is a Unique Paradigm
- 35. Three waves
- 38. Retail 7 of top 10 Finance 20 of top 25 7 of top 10 Software Hospitality 3 of top 5 Telco 4 of top 5 Airlines 3 of top 5 Logistics 3 of top 5 76% FORTUNE 100 have adopted or piloted Neo4j
- 39. Real-Time Recommendations Dynamic Pricing Artificial Intelligence & IoT-applications Fraud Detection Network Management Customer Engagement Supply Chain Efficiency Identity and Access Management Relationship-Driven Applications
- 40. 40 • Record “Cyber Monday” sales • About 35M daily transactions • Each transaction is 3-22 hops • Queries executed in 4ms or less • Replaced IBM Websphere commerce • 300M pricing operations per day • 10x transaction throughput on half the hardware compared to Oracle • Replaced Oracle database • Large postal service with over 500k employees • Neo4j routes 7M+ packages daily at peak, with peaks of 5,000+ routing operations per second. Handling Large Graph Work Loads for Enterprises Real-time promotion recommendations Marriott’s Real-time Pricing Engine Handling Package Routing in Real-Time
- 41. Home Security Internet of things Institutional Memory Entertainment Recommendations Home Operations Personalization Voice Enabled Smart Home
- 42. More Data Enables More Use Cases
- 43. Data Network Effect “A product, generally powered by machine learning, becomes smarter as it gets more data from your users. The more users use your product, the more data they contribute; the more data they contribute, the smarter your product becomes.” — Matt Turck
- 44. What’s Enabling All of This?
- 45. A year ago…
- 46. 46 Neo4j Graph Platform Development & Administration Analytics Tooling BUSINESS USERS DEVELOPERS ADMINS Graph Analytics Graph Transactions Data Integration Discovery & Visualization DATA ANALYSTS DATA SCIENTISTS Drivers & APIs APPLICATIONS AI
- 47. Neo4j Bloom
- 50. Graph Analytics Graph Algorithms Cypher for Apache Spark™ Graph-Enhanced AI & ML Similarity ML
- 51. Algorithms in Neo4j Pathfinding & Search Centrality / Importance Community Detection Similarity & ML Workflow 2019 Q1neo4j.com/resources +30
- 52. AI & Graphs
- 53. EVIDENCE BASED MACHINE LEARNING SYSTEMS PRESCRIPTE ANALYTICS NATURAL LANGUAGE GENERATION “Yankees” “Giants” “Penguins” “Jets” “Bears” “Red Soxs” NLP/TEXT MINING PREDICITVE ANALYTICS RECOMMENDATION ENGINES DEEP LEARNING
- 54. Graphs Provide Connections & Context for AI
- 55. FATHER_OF DRIVE
- 56. Knowledge Graphs
- 57. What Your ML Looks Like Today
- 58. Decisions Machine Learning Pipeline Data records (“Features”)
- 61. “Increasingly we're learning that you can make better predictions about people by getting all the information from their friends and their friends’ friends than you can from the information you have about the person themselves” — Dr. James Fowler Relationships Are Often the Strongest Predictors of Behavior
- 62. Decisions Machine Learning Pipeline Data records
- 63. $ Better Decisions Machine Learning Pipeline
- 66. $ Better Decisions Machine Learning Pipeline • Engineered features when you know what you’re looking for • Feature extraction and selection using graph algorithms • Graph embeddings to feed into DL Graphs add highly predictive features to models; adding accuracy without altering current workflows Graphs can also infer relationships and add data where sparse
- 68. talks on Graph-Enhanced AI & ML recorded at GraphConnect talks on Graph-Enhanced AI & ML during the Spring GraphTour
- 69. Four Pillars of Graph-Enhanced AI 1. Knowledge Graphs Context for Decisions 2. Connected Feature Extraction Context for Credibility 4. AI Explainability3. Graph Accelerated AI Context for Efficiency Context for Accuracy
- 70. Enhance Your Optional ML 70 Knowledge Graphs add context for decisions Graph accelerated ML uses context for efficiency
- 71. Knowledge Graphs 71 GraphConnect speakers 2015-2017
- 72. 56% of enterprise CIOs say iterative model training is the largest ML challenge1 Graph Accelerated ML
- 73. Graph filtering is quite efficient, especially compared to typical manual sub-setting or statistical inference Graph Accelerated ML
- 74. Enhance Your Predictions 74 Connected Features add context to ML for improved accuracy, precision, and recall
- 75. • Transaction Fraud • Anti-money laundering (AML) • Claims Fraud • Credit Fraud • Compliance and investigation 75 Improve the Predictive Power of ML in Fighting Financial Crimes Machine Learning Pipeline Data Machine Learning can help uncover & learn common traits so we can build more predictive models Unfortunately many machine learning methods rely on flat data structures and tables
- 76. Engineering connected features improves Machine Learning by calculating relationship metrics when you know what’s predictive For example, adding how many fraudsters are in someone’s network is faster and simpler using connections Combat Financial Crimes using Connected Features ACCOUNT HOLDER ACCOUNT HOLDER ACCOUNT HOLDER ACCOUNT HOLDER ACCOUNT HOLDER BANK ACCOUNT SSN/ ID NUMBER UNSECURED LOAN BANK ACCOUNT BANK ACCOUNT UNSECURED LOAN PHONE NUMBER CREDIT CARD SSN/ ID NUMBER PHONE NUMBER ACCOUNT HOLDER ACCOUNT HOLDER ACCOUNT HOLDER ADDRESS PHONE NUMBER $ APPLICATION
- 77. ACCOUNT HOLDER ACCOUNT HOLDER ACCOUNT HOLDER ACCOUNT HOLDER ACCOUNT HOLDER 3 Fraudsters – 4 Hops Out 4 Fraudsters – 2 Hops Out BANK ACCOUNT SSN/ ID NUMBER UNSECURED LOAN BANK ACCOUNT BANK ACCOUNT UNSECURED LOAN PHONE NUMBER CREDIT CARD SSN/ ID NUMBER PHONE NUMBER ACCOUNT HOLDER ACCOUNT HOLDER ACCOUNT HOLDER ADDRESS PHONE NUMBER $ APPLICATION Combat Financial Crimes using Connected Feature Engineering
- 78. Decisions $ Better Decisions Graphs add highly predictive features to models; adding accuracy without altering current workflows Machine Learning Pipeline Machine Learning Pipeline Traditional methods based on ”flat data” simplify, or leave out entirely, predictive relationship and network data
- 79. 79
- 80. Explain Why 80 Graphs add context to AI decisions for explainability and credibility
- 82. A Highly Connected Future
- 84. Your Homework - Connect