Graph All The Things! - Andreas Kollegger @ Neo4j GraphDays: #AllYouCanGraph Palo Alto 2014

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Recent years have seen an explosion of technologies for managing, processing and analyzing graphs. While the most well known users of graph technologies have been social web properties such as Facebook and LinkedIn, a quiet revolution has been steadily spreading across other industries. In this last 18 months, more than 30 of the Global 2000, and many times as many startups, have quietly been working to apply graphs to a wide array of business-critical use cases.

For example: one of the world’s top parcel delivery carriers wasn’t going to be able to handle Christmas volumes last year because of numerous challenges stemming from online ordering. The solution? Replace the legacy routing system with a graph database, which now routes 5M packages per day in real time: faster and more efficiently than its relational cousins ever could. One of the top investment banks now onboards traders using an identity & access management system based on graphs. Media metadata turns out to be best represented as a graph; and consumers respond well to the opportunity to visually navigate the graph (such as is done by the app Discovr Music). Similar trends are developing in telecommunications, healthcare, human resources, gaming, and many more.

We are entering an era of connected data: where those companies that can master the connections between their data – the lines and patterns linking the dots, and not just the dots – will outperform the companies that fail to recognize connectedness.

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Graph All The Things! - Andreas Kollegger @ Neo4j GraphDays: #AllYouCanGraph Palo Alto 2014

  1. 1. Graph all the Things A story of data and relationships Andreas Kollegger Product Designer @akollegger
  2. 2. 2005: Lusaka, Zambia
  3. 3. 2005: Lusaka, Zambia
  4. 4. ALS Lou Gehrig's Disease Amyotrophic lateral sclerosis motor neurone disease Charcot disease MND ICD-10 G12.2 ICD-9 335.20 OMIM 105400 2005: Lusaka, Zambia
  5. 5. ALS Lou Gehrig's Disease Amyotrophic lateral sclerosis motor neurone disease Charcot disease MND ICD-10 G12.2 ICD-9 335.20 OMIM 105400 2005: Lusaka, Zambia ALS Lou Gehrig's Disease Amyotrophic lateral sclerosis motor neurone disease Charcot disease MND ICD-10 G12.2 ICD-9 335.20 OMIM 105400
  6. 6. ALS Lou Gehrig's Disease Amyotrophic lateral sclerosis motor neurone disease Charcot disease MND ICD-10 G12.2 ICD-9 335.20 OMIM 105400 2005: Lusaka, Zambia SNOMED? ALS Lou Gehrig's Disease Amyotrophic lateral sclerosis motor neurone disease Charcot disease MND ICD-10 G12.2 ICD-9 335.20 OMIM 105400
  7. 7. ALS Lou Gehrig's Disease Amyotrophic lateral sclerosis motor neurone disease Charcot disease MND ICD-10 G12.2 ICD-9 335.20 OMIM 105400 2005: Lusaka, Zambia SNOMED? LOINC? ALS Lou Gehrig's Disease Amyotrophic lateral sclerosis motor neurone disease Charcot disease MND ICD-10 G12.2 ICD-9 335.20 OMIM 105400
  8. 8. ALS Lou Gehrig's Disease Amyotrophic lateral sclerosis motor neurone disease Charcot disease MND ICD-10 G12.2 ICD-9 335.20 OMIM 105400 2005: Lusaka, Zambia SNOMED? LOINC? Non-English terms and local idioms? ALS Lou Gehrig's Disease Amyotrophic lateral sclerosis motor neurone disease Charcot disease MND ICD-10 G12.2 ICD-9 335.20 OMIM 105400
  9. 9. ALS Lou Gehrig's Disease Amyotrophic lateral sclerosis motor neurone disease Charcot disease MND ICD-10 G12.2 ICD-9 335.20 OMIM 105400 2005: Lusaka, Zambia SNOMED? LOINC? Non-English terms and local idioms? ALS Lou Gehrig's Disease Amyotrophic lateral sclerosis motor neurone disease Charcot disease MND ICD-10 G12.2 ICD-9 335.20 OMIM 105400 Categories and Indicators and Related Conditions and Treatments and Drugs?
  10. 10. Hey, who wants to come to Sweden to hack on Neo4j for 6 months?
  11. 11. 2000: Sweden
  12. 12. 2000: Sweden
  13. 13. 2000: Sweden
  14. 14. 2000: Sweden
  15. 15. 2000: Sweden
  16. 16. Graphs are not a new idea, just the right idea right now.
  17. 17. 1736: Königsberg
  18. 18. A B D C 1736: Königsberg
 7 Bridges Problem
  19. 19. A B D C 1736: Königsberg
 7 Bridges Problem
  20. 20. A B D C 1736: Königsberg
 7 Bridges Problem
  21. 21. A B D C 1736: Königsberg
 7 Bridges Problem
  22. 22. A B D C 1 2 3 4 7 6 5 1736: Königsberg
 7 Bridges Problem
  23. 23. A B D C 1 2 3 4 7 6 5 1736: Königsberg
 7 Bridges Problem
  24. 24. Graphs are everywhere!
  25. 25. Neo Technology, Inc Confidential
  26. 26. NOSQL == Not Only SQL
  27. 27. Hint: Relationships Matter Put all of us into a database. ! Ask an RDBMS: “What's the average age of everyone here?” ! Ask Neo4j: “Who should I get to know better?”
  28. 28. Member Group
  29. 29. Member Group 143 Andreas
  30. 30. Member Group 143 Andreas 326 Stockholm 725 San Francisco 981 Boston
  31. 31. Member GroupMember_Group 143 Andreas 326 Stockholm 725 San Francisco 981 Boston
  32. 32. Member GroupMember_Group 143 Andreas 326 Stockholm 725 San Francisco 981 Boston 143 981 143 725 143 326
  33. 33. Andreas India San Francisco Boston 143 326 725 981 143 981 143 725 143 326
  34. 34. Andreas India San Francisco Boston
  35. 35. Property Graph
  36. 36. Nodes Property Graph
  37. 37. Nodes Property Graph
  38. 38. Nodes Member Group Group Group with Labels Property Graph
  39. 39. Nodes Member Group Group Group with Labels Relationships Property Graph
  40. 40. Nodes uid: ABK name: Andreas uid: STK where: Stockholm uid: SFO where: San Francisco uid: BOS where: Boston Member Group Group Group with Labels Relationships with Type MEMBER_OF MEMBER_OF MEMBER_OF since: 2009 since: 2013 since: 2012 Property Graph
  41. 41. Nodes uid: ABK name: Andreas uid: STK where: Stockholm uid: SFO where: San Francisco uid: BOS where: Boston Member Group Group Group with Labels Relationships with Type MEMBER_OF MEMBER_OF MEMBER_OF since: 2009 since: 2013 since: 2012 Properties on both Property Graph
  42. 42. What about the wolves?!? Tell me about the wolves!
  43. 43. How Wolves Change Rivers:
 Research Results
  44. 44. How Wolves Change Rivers:
 Known Influences Modeled as a Graph
  45. 45. How Wolves Change Rivers:
 Known Influences Modeled as a Graph
  46. 46. How Wolves Change Rivers:
 Query for Trophic Cascades ! MATCH path = (:Animal {Entity:"Wolves"})-[*]->(:Landscape {Entity:"Rivers"}) WITH extract(node IN nodes(path) | node.Yellowstone) AS factor, rand() AS number RETURN factor AS How_Wolves_Affect_RiverStability ORDER BY number LIMIT 5
  47. 47. How Wolves Change Rivers:
 Query for Trophic Cascades ! MATCH path = (:Animal {Entity:"Wolves"})-[*]->(:Landscape {Entity:"Rivers"}) WITH extract(node IN nodes(path) | node.Yellowstone) AS factor, rand() AS number RETURN factor AS How_Wolves_Affect_RiverStability ORDER BY number LIMIT 5
  48. 48. Thank you! Think about relationships, Follow paths, Find your graph

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