Successfully reported this slideshow.
Your SlideShare is downloading. ×

Graph Thinking: Why it Matters

Graph Thinking: Why it Matters

Download to read offline

Using data relationships to make connections between individual data records transforms the data you already have into something much more powerful. This webinar will explain how both young and established companies have adopted graph thinking - and how they’ve risen to dominate their fields.

Using data relationships to make connections between individual data records transforms the data you already have into something much more powerful. This webinar will explain how both young and established companies have adopted graph thinking - and how they’ve risen to dominate their fields.

Advertisement
Advertisement

More Related Content

Advertisement
Advertisement

Graph Thinking: Why it Matters

  1. 1. Graph Thinking Andreas Kollegger Product Designer
  2. 2. Graph Thinking the missing link in your data
  3. 3. Chilenje Clinic Lusaka, Zambia
  4. 4. • Patient Care • Decision Support • Public Health Research • WHO Reporting Medical Record System
  5. 5. • Current data • Prior history • Local context • How is the data related? • What patterns emerge from the relationships? • Which patterns matter? • "Assessment" makes connections, creating new data How does this work?
  6. 6. Some data is missing...
  7. 7. • Epidemiology • Cultural Norms • Environmental Factors • Agricultural Practices • Patient Relationships • Doctor Relationships • Related Research • ...
  8. 8. How much data do you need?
  9. 9. "The person with the most data, wins." – Tim O'Reilly
  10. 10. 1. Close relationships determine relevance Number of relationships increases importance What data do you need?
  11. 11. Naturally, that leads to pattern matching...
  12. 12. The internet
  13. 13. Genetic Ancestry of One Single Corn Variety
  14. 14. Andreas' Linkedin Network Andreas Kollegger
  15. 15. aph Thinking has created some of the most successful companies in the wo
  16. 16. WHAT IS A GRAPH?
  17. 17. A way of representing data DATA DATA
  18. 18. Relational Database Good for: Well-understood data structures that don’t change too frequently A way of representing data Known problems involving discrete parts of the data, or minimal connectivity
  19. 19. Graph Database Relational Database Good for: Well-understood data structures that don’t change too frequently Known problems involving discrete parts of the data, or minimal connectivity A way of representing data Good for: Dynamic systems: where the data topology is difficult to predict Dynamic requirements: the evolve with the business Problems where the relationships in data contribute meaning & value
  20. 20. THE PROPERTY GRAPH DATA MODEL
  21. 21. A Graph Is
  22. 22. ROAD TRAFFIC LIGHTS A Graph Is
  23. 23. HAS HOTEL ROOMS AVAILABLE A Graph Is
  24. 24. KNOWS KNOWS WORKS_AT WORKS_AT WORKS_AT COMPANY STANFORD STUDIED_AT NEO COLUMBIA STUDIED_AT NAME:ANNE A Graph RELATIONSHIPS NODE PROPERTY
  25. 25. A Graph NAME:ANNE
  26. 26. Graph Thinking in Practice Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
  27. 27. Graph Thinking in Practice Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations GRAPH THINKING: Real Time Recommendations
  28. 28. “As the current market leader in graph databases, and with enterprise features for scalability and availability, Neo4j is the right choice to meet our demands.”Marcos Wada Software Developer, Walmart Graph Thinking with Neo4j Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
  29. 29. Graph Thinking in Practice Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations GRAPH THINKING: Master Data Management
  30. 30. Neo4j is the heart of Cisco HMP: used for governance and single source of truth and a one-stop shop for all of Cisco’s hierarchies. Graph Thinking with Neo4j Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
  31. 31. Graph Thinking in Practice Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations GRAPH THINKING: Fraud Detection
  32. 32. “Graph databases offer new methods of uncovering fraud rings and other sophisticated scams with a high-level of accuracy, and are capable of stopping advanced fraud scenarios in real-time.”Gorka Sadowski Cyber Security Expert Graph Thinking with Neo4j Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
  33. 33. GRAPH THINKING: Graph Based Search IN Graph Thinking in Practice Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
  34. 34. Uses Neo4j to manage the digital assets inside of its next generation in-flight entertainment system. Graph Thinking with Neo4j Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
  35. 35. Graph Thinking in Practice Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations GRAPH THINKING: Network & IT-Operations
  36. 36. Uses Neo4j for network topology analysis for big telco service providers Graph Thinking with Neo4j Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
  37. 37. GRAPH THINKING: Identity And Access Management Graph Thinking in Practice Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
  38. 38. UBS was the recipient of the 2014 Graphie Award for “Best Identify And Access Management App” Graph Thinking with Neo4j Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
  39. 39. WHY GRAPH THINKING?
  40. 40. Intuitivness Speed Agility
  41. 41. Intuitivness Speed Agility
  42. 42. Intuitivness
  43. 43. Intuitivness Speed Agility
  44. 44. Speed “We found Neo4j to be literally thousands of times faster than our prior MySQL solution, with queries that require 10-100 times less code. Today, Neo4j provides eBay with functionality that was previously impossible.” - Volker Pacher, Senior Developer “Minutes to milliseconds” performance Queries up to 1000x faster than RDBMS or other NoSQL
  45. 45. Intuitivness Speed Agility
  46. 46. A Naturally Adaptive Model A Query Language Designed for Connectedness + =Agility
  47. 47. Cypher Typical Complex SQL Join The Same Query using Cypher MATCH (boss)-[:MANAGES*0..3]->(sub), (sub)-[:MANAGES*1..3]->(report) WHERE boss.name = “John Doe” RETURN sub.name AS Subordinate, count(report) AS Total Project Impact Less time writing queries • More time understanding the answers • Leaving time to ask the next question Less time debugging queries: • More time writing the next piece of code • Improved quality of overall code base Code that’s easier to read: • Faster ramp-up for new project members • Improved maintainability & troubleshooting
  48. 48. Ann DanLoves CYPHER
  49. 49. Ann DanLoves
  50. 50. Ann DanLoves (Dan)(Ann) -[:LOVES]->
  51. 51. Ann DanLoves (:Person {name:”Ann"}) –[:LOVES]-> (:Person {name:"Dan"})
  52. 52. Ann DanLoves (:Person {name:”Ann"}) –[:LOVES]-> (:Person {name:"Dan"})
  53. 53. Ann DanLoves Node Relationship Node (:Person {name:"Ann"}) –[:LOVES]-> (:Person {name:"Dan"})
  54. 54. Query: Whom does Ann love? MATCH (:Person {name:"Ann"})–[:LOVES]->(whom) RETURN whom
  55. 55. Users Love Cypher
  56. 56. Graph Thinking focuses on relationships to turn data into information and uses patterns to find meaning
  57. 57. It's all about relationships & patterns
  58. 58. THANK YOU!

Editor's Notes

  • increasing complexity
  • 100 billion neurons
    1,000 trillion synaptic connections
  • 100 billion neurons
  • And deriving value from data-relationships is exactly what some of the most successful companies in the world have done.

    Google created perhaps the most valuable advertising system of all time on top of their search-enginge, which is based on relationships between webpages.
    Linkedin created perhaps the most valuable HR-tool ever based on relationships amongst professional
    And this is also what pay-pal did, creating a peer-to-peer transaction service, based on relationships.
  • First, not everyone in the room would know what a graph is.
  • What this means for your data structure
  • First, not everyone in the room would know what a graph is.
  • A graph is connected data.
    Which essentially means – datapoints that have relationships with other datapoints.
  • For example, a road could have traffic jams and traffic lights
  • Or a hotel that has rooms, which have availability
  • Or it could be people who know other people – who know other people.. who studied together, who work at the same place – who studied with other people, who works somewhere else… etc.
  • The interesting thing is what happens when you start to add more and more relationships to these graphs, and these things start to take off at scale…
  • …forming an extremely powerful foundation from which you can derive value.
  • First, not everyone in the room would know what a graph is.
  • First, not everyone in the room would know what a graph is.
  • The obligatory “Ann Loves Dan” example
  • “And people love it”
  • Thank you for listening!

×