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Neo4j GraphTour Toronto Opening Keynote

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Lance Walter, CMO, Neo4j

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Neo4j GraphTour Toronto Opening Keynote

  1. 1. Lance Walter, CMO, Neo4j Neo4j GraphTour Toronto June 25, 2019
  2. 2. Welcome! #graphtour #neo4j
  3. 3. Agenda • Graphs 101 • State of the Graph Union • The Future of Graphs
  4. 4. Frederik Obermaier, Süddeutsche Zeitung, on the importance of networks in journalism. From Panel at Columbia University Feb 23, 2018. “I’ve only come across 3 or 4 stories in my career that weren’t about networks.”
  5. 5. ACCOUNT HAS REGISTERED ADDRESS PERSON IS_OFFICER_OF PERSON NAME STREET BANK WITH LIVES_AT LIVES_AT NAME COMPANY BANK BAHAMAS 2.6 TB 11.5 million documents Emails, Scanned Documents, Bank Statements etc…
  6. 6. 2.6 TB 11.5 million documents Emails, Scanned Documents, Bank Statements etc… Person B Bank US Account 123 Person A Acme Inc Bank Bahamas Address X HAS_ACCOUNT REGISTERED IS_OFFICER_OF WITH LIVES_AT LIVES_AT NODE RELATIONSHIP
  7. 7. 2.6 TB 11.5 million documents Emails, Scanned Documents, Bank Statements etc…
  8. 8. ICIJ Pulitzer Price Winner 2017
  9. 9. Common Graph Use Cases Fraud Detection Real-Time Recommendations Network & IT Operations Master Data Management Knowledge Graph Identity & Access Management
  10. 10. Common Graph Use Cases airbnb Fraud Detection Real-Time Recommendations Network & IT Operations Master Data Management Knowledge Graph Identity & Access Management
  11. 11. “Forrester estimates that over 25% of enterprises will be using graph databases by 2017.” Forrester, 2014
  12. 12. 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 2019
  13. 13. >50%of enterprises were using graph databases In 2017 Source: Forrester Vendor Landscape: Graph Databases, October 6, 2017
  14. 14. Trend No. 5: Graph … The application of graph processing and graph DBMSs will grow at 100 percent annually through 2022 to continuously accelerate data preparation and enable more complex and adaptive data science.  … Graph analytics will grow in the next few years due to the need to ask complex questions across complex data, which is not always practical or even possible at scale using SQL queries. https://www.gartner.com/en/newsroom/press-releases/2019-02-18-gartner-identifies-top-10-data-and-analytics-technolo February 18, 2019
  15. 15. The Hadoop Market is Unsteady
  16. 16. From Collections to Connections “50% of database workloads will move to graphs in the next 19 years” Michael Moore, Ph.D. EY
  17. 17. Strictly ConfidentialStrictly Confidential Strategic initiative, led by Thomas Kurian, CEO of Google Cloud • Goal to be #2 Enterprise Cloud as the “open source friendly” alternative to AWS • Work with known/proven leaders across key areas • Neo4j/GCP integrated solution beta by EoY 2019 • Initial release of Neo4j DBaaS will be available via the Google marketplace 23 Google Cloud Partnership • Fully managed services running in the cloud, with best efforts made to optimize performance and latency between the service and application. • A single user interface to manage apps, which includes the ability to provision and manage the service from the Google Cloud Console. • Unified billing, so you get one invoice from Google Cloud that includes the partner’s service. • Google Cloud support to manage and log support tickets in a single window and not have to deal with different providers.
  18. 18. 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 are piloting Neo4j
  19. 19. 2000+
  20. 20. Neo4j Startup Program Expansion • Free access for startups with up to 50 employees;
 under $3M in revenue • Neo4j Enterprise Edition • Neo4j Bloom • Apply at http://neo4j.com/startup-program • Notable alumni include: Medium
  21. 21. Background • Over 7M citizens suffer from Diabetes • Connecting over 400 researchers • Incorporates over 50 databases, 100k’s of Excel workbooks, 30 database of biological samples • Sought to examine disease from as many angles as possible. Business Problem • Genes are connected by proteins or to metabolites, and patients are connected with their diets, etc… • Needed to improve the utilization of immensely technical data • Needed to cater to doctors and researchers with simple navigation, communication and connections of the graph. Solution and Benefits • Dr. Alexander Jarasch, Head of Bioinformatics and Data Management • Scientists can conduct parallel research without asking the same questions or repeating tests • Built views like a liver sample knowledge graph DZD - German Center for Diabetes Research Medical Genomic Research!28 EE Customer since 2016 Q
  22. 22. Background • Fortune 100 heavy equipment manufacturer • 27 Million warranty & service documents parsed • Foundation for AI-based supply chain management Business Problem • Improve maintenance predictability • Need a knowledge base for 27 million warranty documents and maintenance orders • Graphs gather context for AI to identify ‘prime examples’ of connections among parts, suppliers, customers and their mechanics anticipate when equipment will need servicing and by whom. Solution and Benefits • Text to knowledge graph • Common ontology for complaints, symptoms & parts • Anticipates when equipment will need servicing • Improves customer and brand satisfaction • Maximizes lifespan and value of equipment Caterpillar Heavy Equipment Manufacturing Parts Assembly & Equipment Maintenance!29
  23. 23. Background • Social network of 10M graphic artists • Peer-to-peer evaluation of art and works-in-progress • Job sourcing site for creatives • Massive, millions of updates (reads & writes) to Activity Feed • 150 Mongos to 48 Cassandras to 3 Neo4j’s! Business Problem • Artists subscribe, appreciate and curate “galleries” of works of their own and from other artists • Activities Feed is how everyone receives updates • 1st implementation was 150 MongoDB instances • 2nd implementation shrunk to 48 Cassandras, but it was still too slow and required heavy IT overhead Solution and Benefits • 3rd implementation shrunk to 3 Neo4j instances • Saved over $500k in annual AWS fees • Reduced data footprint from 50TB to 40GB • Significantly easier to introduce new features like, “New projects in you Network” Adobe Behance Social Network of 10M Graphic Artists Social Network!30 EE Customer since 2016 Q
  24. 24. Home Security Internet of things Institutional Memory Entertainment Recommendations Home Operations Personalization Voice Enabled Smart Home
  25. 25. !32
  26. 26. Background • Largest Cable TV & Internet Provider in US • 3rd Largest network on the planet • xFi is consumer experience in 3M houses • Internet, router, devices, security, voice & telephony • Transformational customer experience Business Problem • Integrate all experience in a smart home • Create innovative ideas based on cross-platform and household member preferences • Add integrated value of xFinity triple play & quad- play services (internet, VoIP, cable TV & home security) Solution and Benefits • Custom content per household member • Security reminders (kids are home, garage left open) • Serves millions of households • Makes content recommendations based on occupant, time of day, permissions and preferences • Has Siri-like voice commands COMCAST Xfinity xFi TELECOMMUNICATIONS Smart Home / Internet of Things!33 EE Customer since 2016 Q
  27. 27. AI & Graphs
  28. 28. 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
  29. 29. Strictly ConfidentialStrictly Confidential The Market Sees Strong Synergy between Graphs and Artificial Intelligence 36 AI research papers focused on graphs SURGING INTEREST New Book: 20K Downloads in first 2 weeks, ⅓ Net-new CONNECTED CONTEXT FOR AI/ML CUSTOMER TRACTION German Center for Diabetes Research Dozens of AI deals in the pipeline
  30. 30. Graphs Provide Connections & Context for AI
  31. 31. Knowledge Graphs
  32. 32. What Your ML Looks Like Today
  33. 33. Data Sources
  34. 34. Decisions Machine Learning Pipeline Data records (“Features”)
  35. 35. “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
  36. 36. Decisions Machine Learning Pipeline Data records
  37. 37. $ Better Decisions Machine Learning Pipeline
  38. 38. Feature Extraction
  39. 39. Connected Feature Extraction
  40. 40. Thank You! http://neo4j.com
  41. 41. #neo4j@lancewalter #neo4j

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