Graph Thinking: Why it Matters
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The document discusses graph thinking and its applications in various fields such as patient care, public health, and data management. It emphasizes the importance of understanding relationships in data to derive meaningful insights and improve decision-making, showcasing how graph databases like Neo4j enhance real-time recommendations, fraud detection, and master data management. The document highlights the efficiency and adaptability of graph databases compared to traditional relational databases, promoting the idea that 'the person with the most data wins.'
![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](https://image.slidesharecdn.com/graph-thinking-for-everyone-160212211942/85/Graph-Thinking-Why-it-Matters-50-320.jpg)
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(:Person {name:”Ann"}) –[:LOVES]-> (:Person {name:"Dan"})](https://image.slidesharecdn.com/graph-thinking-for-everyone-160212211942/85/Graph-Thinking-Why-it-Matters-55-320.jpg)
![Ann DanLoves
Node Relationship Node
(:Person {name:"Ann"}) –[:LOVES]-> (:Person {name:"Dan"})](https://image.slidesharecdn.com/graph-thinking-for-everyone-160212211942/85/Graph-Thinking-Why-it-Matters-56-320.jpg)
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MATCH (:Person {name:"Ann"})–[:LOVES]->(whom)
RETURN whom](https://image.slidesharecdn.com/graph-thinking-for-everyone-160212211942/85/Graph-Thinking-Why-it-Matters-57-320.jpg)
Graph Thinking: Why it Matters
- 1. Graph Thinking Andreas Kollegger Product Designer
- 2. Graph Thinking the missing link in your data
- 4. • Patient Care • Decision Support • Public Health Research • WHO Reporting Medical Record System
- 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?
- 7. • Epidemiology • Cultural Norms • Environmental Factors • Agricultural Practices • Patient Relationships • Doctor Relationships • Related Research • ...
- 8. How much data do you need?
- 9. "The person with the most data, wins." – Tim O'Reilly
- 10. 1. Close relationships determine relevance Number of relationships increases importance What data do you need?
- 11. Naturally, that leads to pattern matching...
- 14. The internet
- 15. Genetic Ancestry of One Single Corn Variety
- 16. Andreas' Linkedin Network Andreas Kollegger
- 17. aph Thinking has created some of the most successful companies in the wo
- 18. WHAT IS A GRAPH?
- 19. A way of representing data DATA DATA
- 20. 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
- 21. 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
- 23. A Graph Is
- 29. Graph Thinking in Practice Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
- 30. 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
- 31. “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
- 32. 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
- 33. 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
- 34. 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
- 35. “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
- 36. 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
- 37. 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
- 38. 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
- 39. 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
- 40. 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
- 41. 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
- 45. Intuitivness
- 47. 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
- 49. A Naturally Adaptive Model A Query Language Designed for Connectedness + =Agility
- 50. 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
- 52. Ann DanLoves
- 54. Ann DanLoves (:Person {name:”Ann"}) –[:LOVES]-> (:Person {name:"Dan"})
- 55. Ann DanLoves (:Person {name:”Ann"}) –[:LOVES]-> (:Person {name:"Dan"})
- 56. Ann DanLoves Node Relationship Node (:Person {name:"Ann"}) –[:LOVES]-> (:Person {name:"Dan"})
- 57. Query: Whom does Ann love? MATCH (:Person {name:"Ann"})–[:LOVES]->(whom) RETURN whom
- 59. Graph Thinking focuses on relationships to turn data into information and uses patterns to find meaning
- 60. It's all about relationships & patterns
- 61. THANK YOU!
Editor's Notes
- #5: increasing complexity
- #13: 100 billion neurons 1,000 trillion synaptic connections
- #14: 100 billion neurons
- #18: 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.
- #19: First, not everyone in the room would know what a graph is.
- #20: What this means for your data structure
- #23: First, not everyone in the room would know what a graph is.
- #24: A graph is connected data. Which essentially means – datapoints that have relationships with other datapoints.
- #25: For example, a road could have traffic jams and traffic lights
- #26: Or a hotel that has rooms, which have availability
- #27: 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.
- #28: 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…
- #29: …forming an extremely powerful foundation from which you can derive value.
- #43: First, not everyone in the room would know what a graph is.
- #52: First, not everyone in the room would know what a graph is.
- #53: The obligatory “Ann Loves Dan” example
- #60: “And people love it”
- #63: Thank you for listening!