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![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-160212211942/75/Graph-Thinking-Why-it-Matters-50-2048.jpg)
![Ann DanLoves
(Dan)(Ann) -[:LOVES]->](https://image.slidesharecdn.com/graph-thinking-for-everyone-160212211942/75/Graph-Thinking-Why-it-Matters-53-2048.jpg)
![Ann DanLoves
(:Person {name:”Ann"}) –[:LOVES]-> (:Person {name:"Dan"})](https://image.slidesharecdn.com/graph-thinking-for-everyone-160212211942/75/Graph-Thinking-Why-it-Matters-54-2048.jpg)
![Ann DanLoves
(:Person {name:”Ann"}) –[:LOVES]-> (:Person {name:"Dan"})](https://image.slidesharecdn.com/graph-thinking-for-everyone-160212211942/75/Graph-Thinking-Why-it-Matters-55-2048.jpg)
![Ann DanLoves
Node Relationship Node
(:Person {name:"Ann"}) –[:LOVES]-> (:Person {name:"Dan"})](https://image.slidesharecdn.com/graph-thinking-for-everyone-160212211942/75/Graph-Thinking-Why-it-Matters-56-2048.jpg)
![Query: Whom does Ann love?
MATCH (:Person {name:"Ann"})–[:LOVES]->(whom)
RETURN whom](https://image.slidesharecdn.com/graph-thinking-for-everyone-160212211942/75/Graph-Thinking-Why-it-Matters-57-2048.jpg)
Uploaded byNeo4j
Graph Thinking: Why it Matters
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.'
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Similar to Graph Thinking: Why it Matters
Graph Thinking: Why it Matters
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- 2. Graph Thinking the missinglink in your data
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- 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?
- 6.
- 7. • Epidemiology • CulturalNorms • Environmental Factors • Agricultural Practices • Patient Relationships • Doctor Relationships • Related Research • ...
- 8. How much data doyou need?
- 9. "The person withthe most data, wins." – Tim O'Reilly
- 10. 1. Close relationshipsdetermine relevance Number of relationships increases importance What data do you need?
- 11. Naturally, that leadsto pattern matching...
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- 15. Genetic Ancestry ofOne Single Corn Variety
- 16. Andreas' Linkedin Network AndreasKollegger
- 17. aph Thinking hascreated some of the most successful companies in the wo
- 18. WHAT IS AGRAPH?
- 19. A way ofrepresenting data DATA DATA
- 20. Relational Database Good for: Well-understood datastructures 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 datastructures 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
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- 29. Graph Thinking inPractice Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
- 30. Graph Thinking inPractice 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 currentmarket 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 inPractice 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 theheart 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 inPractice Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations GRAPH THINKING: Fraud Detection
- 35. “Graph databases offernew 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 BasedSearch 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 tomanage 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 inPractice 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 fornetwork 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 AndAccess 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 therecipient 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
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- 47. Speed “We found Neo4jto 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
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- 49. A Naturally AdaptiveModel A Query Language Designed for Connectedness + =Agility
- 50. Cypher Typical Complex SQLJoin 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
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- 54. Ann DanLoves (:Person {name:”Ann"})–[:LOVES]-> (:Person {name:"Dan"})
- 55. Ann DanLoves (:Person {name:”Ann"})–[:LOVES]-> (:Person {name:"Dan"})
- 56. Ann DanLoves Node RelationshipNode (:Person {name:"Ann"}) –[:LOVES]-> (:Person {name:"Dan"})
- 57. Query: Whom doesAnn love? MATCH (:Person {name:"Ann"})–[:LOVES]->(whom) RETURN whom
- 58.
- 59. Graph Thinking focuses onrelationships to turn data into information and uses patterns to find meaning
- 60. It's all about relationships& patterns
- 61.
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!