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Ketnote: GraphTour Boston

Jeff Morris, Head of Product Marketing, Neo4j

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Ketnote: GraphTour Boston

  1. 1. 1 Welcome to Graph Tour Jeff Morris Head of Product Marketing Neo4j, Inc. @jeffmmorris
  2. 2. I’ve been listening to a lot of books lately…72 and counting Adjacent Possibilities City as a Maze Connecting with PeopleInnovation
  3. 3. 3 Connectedness Is the Common Theme
  4. 4. Connectedness Represented in Graphs 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 Network Operations Social Networks
  5. 5. Who We Are: The Graph Platform Neo4j is an enterprise-grade native graph platform that enables you to: • Store, reveal and query data relationships • Traverse and analyze any levels of depth in real-time • Add context and connect new data on the fly • Performance • ACID Transactions • Agility • Graph Algorithms 5 Designed, built and tested natively for graphs from the start for: • Developer Productivity • Hardware Efficiency • Global Scale • Graph Adoption
  6. 6. The Rise of Connections in Data Networks of People Business Processes Knowledge Networks E.g., Risk management, Supply chain, Payments E.g., Employees, Customers, Suppliers, Partners, Influencers E.g., Enterprise content, Domain specific content, eCommerce content Data connections are increasing as rapidly as data volumes
  7. 7. 7 Real-Time Recommendations Fraud Detection Network & IT Operations Master Data Management Knowledge Graph Identity & Access Management Common Graph Technology Use Cases AirBnb
  8. 8. Software Financial Services Telecom Retail & Consumer Goods Media & Entertainment Other Industries Airbus Over 250 Enterprises and 10s of Thousands of Projects on Neo4j
  9. 9. Neo4j Advantages 9
  10. 10. Store / Retrieve Native Graph Architecture Advantage
  11. 11. Load Data Store / Retrieve Native Graph Architecture Advantage
  12. 12. Load Data Connectedness drives data value Store / Retrieve Neo4j reveals connections in your data Native Graph Architecture Advantage
  13. 13. CAR name: “Dan” born: May 29, 1970 twitter: “@dan” name: “Ann” born: Dec 5, 1975 since: Jan 10, 2011 brand: “Volvo” model: “V70” Neo4j Invented the Labeled Property Graph Model Nodes • Can have Labels to classify nodes • Labels have native indexes Relationships • Relate nodes by type and direction Properties • Attributes of Nodes & Relationships • Stored as Name/Value pairs • Can have indexes and composite indexes MARRIED TO LIVES WITH PERSON PERSON 13
  14. 14. Connectedness Differentiates Neo4j Conceive Code Compute Store Non-Native Graph DBNative Graph DB RDBMS Optimized for graph workloads
  15. 15. Lessons Learned: Ten Year Head Start Native Connectedness Differentiates Neo4j Conceive Code Compute Store Non-Native Graph DBNative Graph DB RDBMS Optimized for graph workloads
  16. 16. 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
  17. 17. The ICIJ and Fraud Detection
  18. 18. Business Problem • Find relationships between people, accounts, shell companies and offshore accounts • Journalists are non-technical • Biggest “Snowden-Style” document leak ever; 11.5 million documents, 2.6TB of data Solution and Benefits • Pulitzer Prize winning investigation resulted in robust coverage of fraud and corruption • PM of Iceland & Pakistan resigned, exposed Putin, Prime Ministers, gangsters, celebrities (Messi) • Led to assassination of journalist in Malta Background • International Consortium of Investigative Journalists (ICIJ), small team of data journalists • International investigative team specializing in cross-border crime, corruption and accountability of power • Works regularly with leaks and large datasets ICIJ Panama Papers INVESTIGATIVE JOURNALISM Fraud Detection / Knowledge Graph18
  19. 19. ACCOUNT ADDRESS PERSON PERSON NAME STREET BANK NAME COMPANY BANK BAHAMAS
  20. 20. Bank US Account Person A Company Bank Bahamas AddressNODE RELATIONSHIP Person B
  21. 21. ICIJ Pulitzer Price Winner 2017
  22. 22. Business Problem • Find relationships between people, corporations, accounts, shell companies and offshore accounts • Journalists are non-technical • 2017 Leak from Appleby tax sheltering law firm matched 13.4 million account records with public business registrations data from across Caribbean Solution and Benefits • Exposed tax sheltering practices of Apple, Nike • Revealed hidden connections among politicians and nations, like Wilbur Ross & Putin’s son in law • Triggered government tax evasion investigations in US, UK, Europe, India, Australia, Bermuda, Canada and Cayman Islands within 2 days. Background • International Consortium of Investigative Journalists (ICIJ), Pulitzer Prize winning journalists • Fourth blockbuster investigation using Neo4j to reveal connections in text-based, and account- based data leaked from offshore law firms and government records about the “1% Elite” • Appends Neo4j-based, “Offshore Leaks Database” ICIJ Paradise Papers INVESTIGATIVE JOURNALISM Fraud Detection / Knowledge Graph22
  23. 23. Paradise Papers Metadata Model
  24. 24. Common Thread: Density Drives Value In Graphs Metcalfe’s Law of the Network (V=n2) 5 hops = Less Value 100’s of hops More Value
  25. 25. Entity Linking Analysis of relationships to detect organized crime and collusion 5. Endpoint-Centric Analysis of users and their end-points 1. Navigation Centric Analysis of navigation behavior and suspect patterns 2. Account-Centric Analysis of anomaly behavior by channel 3. PC:s Mobile Phones IP-addresses User ID:s Comparing Transaction Identity Vetting Augment Methods by Examining Connected Data DISCRETE ANALYSIS CONNECTED ANALYSIS Cross Channel Analysis of anomaly behavior correlated across channels 4.
  26. 26. 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 Fraud Transactions as an Organized Ring At first glance, each account holder looks normal. Each has multiple accounts…
  27. 27. ACCOUNT HOLDER 2 ACCOUNT HOLDER 3 BANK ACCOUNT BANK ACCOUNT BANK ACCOUNT ADDRESS PHONE NUMBER PHONE NUMBER SSN 2 UNSECURED LOAN SSN 2 UNSECURED LOAN ACCOUNT HOLDER 1 Modeling Fraud Transactions as an Organized Ring AHA! But they share common phone numbers, addresses and SSNs! These are difficult to detect using traditional methods CREDIT CARD
  28. 28. Evolution of Graphs in Digital Transformation 29
  29. 29. CONSUMER DATA PRODUCT DATA PAYMENT DATA SOCIAL DATA SUPPLIER DATA The next wave of competitive advantage will be all about using connections to identify and build knowledge Graphs in The Age of Connections
  30. 30. Social Graphs: Roles & Projects around Enterprise Data Hub Data Scientists Real-time Graph traversal Applications Developers & Prod Managers Analysts and Business Users Chief Officers of … Compliance, Data, Digital, Information, Innovation, Marketing, Operations, Risk & Security… Big Data IT & Architecture ID, Auth & Security Network & IT Ops Metadata Management 360⁰ Marketing Customer 360 Real-time Cybersecurity Account navigation • Multiple paths through organization • Graphs have strong appetite for data to add nodes & increase density of relationships • Value of graph increase according to Metcalfe’s Law (V=n2) • Customer applications iterate every 3 months
  31. 31. Evolution of Graphs in Digital Transformation 32 1st Graph Connects “like” objects People, computer networks, telco, etc
  32. 32. Disruptors of the SMAC Generation Social Network Search Social, Mobile, Analytics, & Cloud technologies are all Graph-based innovations Maps
  33. 33. Evolution of Graphs in Digital Transformation 34 Context Paths 1st Graph Desire for more context to follow connections Connects like objects People, computer networks, telco, etc
  34. 34. 35 Disruptors of the SMAC Generation Social Network Rideshare Cloud Computing Retail Personal Entertainment Delivery Autonomous Vehicles Media consumption Facial recognition AI-Powered Investing Travel Real estate utility Search CollaborationAd Networks Mobile Computing Social, Mobile, Analytics, & Cloud technologies are all Graph-based innovations Maps
  35. 35. Knowledge Graphs at NASA 36
  36. 36. 37 I was raised on the Space Program
  37. 37. “Lessons Learned Database” A half-century of collective NASA engineering knowledge “How do we make sure the command module doesn’t tip over and sink?”
  38. 38. Let’s Hear a Few Stories — David Meza, Chief Knowledge Architect at NASA “Neo4j saved well over two years of work and one million dollars of taxpayer funds.” Impact
  39. 39. Evolution of Graph Adoption 41 Context Paths 1st Graph Cross-connect Connect unlike objects such as people to products, locations Mobile App Explosion Recommendation Engines Fraud Detectors Desire for more context to follow connections Connects like objects People, computer networks, telco, etc
  40. 40. Transformative Customer Experience
  41. 41. 43 • 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. Transformative Customer Experiences Real-time promotion recommendations Marriott’s Real-time Pricing Engine Handling Package Routing in Real-Time
  42. 42. Background • Personal shopping assistant • Converses with buyer via text, picture and voice to provide real-time recommendations • Combines AI and natural language understanding (NLU) in Neo4j Knowledge Graph • First of many apps in eBay's AI Platform Business Problem • Improve personal context in online shopping • Transform buyer-provided context into ideal purchase recommendations over social platforms • "Feels like talking to a friend" Solution and Benefits • 3 developers, 8M nodes, 20M relationships • Needed high-performance traversals to respond to live customer requests • Easy to train new algorithms and grow model • Generating revenue since launch eBay ShopBot ONLINE RETAIL Knowledge Graph powers Real-Time Recommendations44 EE Customer since 2016 Q3
  43. 43. Case Study: Knowledge Graphs at eBay
  44. 44. Case Study: Knowledge Graphs at eBay
  45. 45. Case Study: Knowledge Graphs at eBay
  46. 46. Case Study: Knowledge Graphs at eBay
  47. 47. Bags Case Study: Knowledge Graphs at eBay
  48. 48. Men’s Backpack Handbag Case Study: Knowledge Graphs at eBay
  49. 49. https://shopbot.ebay.com/ Try it out at: Case Study: Knowledge Graphs at eBay
  50. 50. Customer Graph Product Graph Supply Graph Real-time product recommendations Real-time supply chain management Real-time risk mitigation Region Street Customer Address Phone Customer Email Email Address Phone Product Product Category Product Category Store Street Store The Graph Behind Online Stores
  51. 51. Evolution of Graph Adoption 53 Context Paths Graph Layers 1st Graph Cross-connect Adjacent graph layers inspire new innovations Metadata / Risk Management Knowledge Graphs AI- Powered Customer Experiences Connect unlike objects such as people to products, locations Desire for more context to follow connections Connects like objects People, computer networks, telco, etc
  52. 52. Thomson Reuters Graph 54 • Data Fusion for Portfolio Managers • Graph layers
  53. 53. Architecture Patterns for Multiple Graphs Layers From Disparate Silos To Cross-Silo Connections From Tabular Data To Connected Data From Data Lake Analytics to Real-Time Operations
  54. 54. In-Q-Tel’s Mission Economy 56 • Venture Capital sponsored by National Intelligence • Decomposes and reassembles technology stacks into common “genome” vocabulary • Matches mission problems to technology assemblies and vendors • Evaluates tech across communications, Bio tech, robotics, software, hardware, IoT • Faster evaluations, better innovations
  55. 55. Evolution of Graph Adoption 57 Context Paths Auto-Graphs Graph Layers 1st Graph Cross-connect Cross-tech applications Internet of Things operations Transparent Neural Networks (How Tensorflow thinks) Blockchain-managed systems Adjacent graph layers inspire new innovations Connect unlike objects such as people to products, locations Desire for more context to follow connections Connects like objects People, computer networks, telco, etc
  56. 56. 58
  57. 57. 59
  58. 58. Common Threads & Lessons 61
  59. 59. Common Thread: Graphs Are VERY Hungry for Data Graphs’ appetite to connect more data accelerates the ability to find adjacent innovations Customer iteration cycles from 2 weeks to 3 months
  60. 60. • Connect more users—Technical and non • Graph density creates innovation opportunities • Customer Experience drives transformation initiatives • Agility and iteration accelerates projects • Longstanding experience & community ensure success • The future… IoT, AI & Blockchain are graphs 63 Summary: Graphs are Digital Transformation
  61. 61. Graph Platform for Applications and Analytics 64
  62. 62. 65 Graph Platform: Connects to Many Roles in Enterprise DEVELOPERS ADMINS Graph Analytics Graph Transactions DATA ANALYSTS DATA SCIENTISTS APPLICATIONS Drivers & APIs Data Integration BIG DATA IT Analytics Tooling BUSINESS USERS Discovery & Visualization Development & Administration
  63. 63. Graph Visualizations in Neo4j Browser
  64. 64. Search-Based Discovery and Analysis 67
  65. 65. Lesson: Maintain Agility and Iterate Quickly 68 Design Thinking Sprints Executive Communication Tools
  66. 66. Cypher: Powerful and Expressive Query Language MATCH (:Person { name:“Dan”} ) -[:MARRIED_TO]-> (spouse) MARRIED_TO Dan Ann NODE RELATIONSHIP TYPE LABEL PROPERTY VARIABLE
  67. 67. Finds the optimal path or evaluates route availability and quality • Single Source Short Path • All-Nodes SSP • Parallel paths Evaluates how a group is clustered or partitioned • Label Propagation • Union Find • Strongly Connected Components • Louvain • Triangle-Count Determines the importance of distinct nodes in the network • PageRank • Betweeness • Closeness • Degree Data Science Algorithms
  68. 68. "Neo4j continues to dominate the graph database market.” October, 2017 “The most widely stated reason in the survey for selecting Neo4j was to drive innovation” February, 2018 “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
  69. 69. Enjoy the Day 72
  70. 70. Graph Transformation Maturity 73 Context Paths Auto-Graphs Graph Layers 1st Graph Cross-Connect Cross-tech applications Internet of Things operations Transparent Neural Networks Blockchain-managed systems Adjacent graph layers inspire new innovations Metadata / Risk Management Knowledge Graphs AI- Powered Customer Experiences Connect unlike objects such as people to products, locations Mobile app explosion Recommendation engines Fraud detectors Desire for more context to follow connections Connects like objects People, computer networks, telco, etc

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