Ce diaporama a bien été signalé.
Nous utilisons votre profil LinkedIn et vos données d’activité pour vous proposer des publicités personnalisées et pertinentes. Vous pouvez changer vos préférences de publicités à tout moment.

GraphTour Keynote, Emil Eifrem, CEO and Founder, Neo4j

143 vues

Publié le

Neo4j GraphTour Europe 2019:
GraphTour Keynote, Emil Eifrem, CEO and Founder, Neo4j

Publié dans : Logiciels
  • Soyez le premier à commenter

  • Soyez le premier à aimer ceci

GraphTour Keynote, Emil Eifrem, CEO and Founder, Neo4j

  1. 1. Welcome! #graphtour #neo4j
  2. 2. Agenda • Graphs 101 • State of the Graph Union • The Future of Graphs
  3. 3. 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.”
  4. 4. 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…
  5. 5. 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
  6. 6. 2.6 TB 11.5 million documents Emails, Scanned Documents, Bank Statements etc…
  7. 7. ICIJ Pulitzer Price Winner 2017
  8. 8. Common Graph Use Cases Fraud Detection Real-Time Recommendations Network & IT Operations Master Data Management Knowledge Graph Identity & Access Management
  9. 9. Common Graph Use Cases airbnb Fraud Detection Real-Time Recommendations Network & IT Operations Master Data Management Knowledge Graph Identity & Access Management
  10. 10. “Forrester estimates that over 25% of enterprises will be using graph databases by 2017.” Forrester, 2014
  11. 11. 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
  12. 12. >50%of enterprises were using graph databases In 2017 Source: Forrester Vendor Landscape: Graph Databases, October 6, 2017
  13. 13. 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
  14. 14. 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
  15. 15. 2000+
  16. 16. News: 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
  17. 17. News: 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
  18. 18. AI & Graphs
  19. 19. 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
  20. 20. Graphs Provide Connections & Context for AI
  21. 21. FATHER_OF DRIVE WORKS_ATM ARRIED_TO PLAYS WATCHES BORN_IN
  22. 22. Knowledge Graphs
  23. 23. What Your ML Looks Like Today
  24. 24. Data Sources
  25. 25. Decisions Machine Learning Pipeline Data records (“Features”)
  26. 26. “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
  27. 27. Decisions Machine Learning Pipeline Data records
  28. 28. $ Better Decisions Machine Learning Pipeline
  29. 29. Feature Extraction
  30. 30. Connected Feature Extraction
  31. 31. 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
  32. 32. Thank You! http://neo4j.com
  33. 33. @emileifrem #neo4j@emileifrem #neo4j

×