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Neo4j GraphTalk Oslo - Building Intelligent Solutions with Graphs

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Neo4j GraphTalk Oslo 2019
Dinuke Abeysekera, Neo4j

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Neo4j GraphTalk Oslo - Building Intelligent Solutions with Graphs

  1. 1. Building Intelligent Solutions with Graphs Dinuke Abeysekera Pre-sales Engineer, Nordics
  2. 2. • Neo4j Services • Solutions and Managed Services • Adding AI/ML to Solutions • Real world examples and best practices 2 Agenda
  3. 3. 3 Neo4j Services
  4. 4. 4 Neo4j PS in the real world Solution Delivery and Management • Packaged Services • Typically 5-25 days • Neo4j advises • Customer builds • 80% of projects • Custom Scoped • 50+ man days • Neo4j delivers • Customer supports • 20% of projects
  5. 5. PROFESSIONAL SERVICES GRAPH ACADEMY SOLUTIONS CUSTOMER SUPPORT ● Packaged Services ● Staff Augmentation ● Project/Solution Delivery ● Class room training ● Online/Virtual training ● Certification ● Innovation Labs ● Solution Workshops ● Solutions Development ● 24x7x365 & KB ● Platinum support ● Cloud Managed Services ● DBaaS (NEW) ● Agile Solution Support Training Enablement Solution Delivery & Management Organization and offerings
  6. 6. 6 Packaged Services Project Lifecycle Graph Awareness Technical Assessment Solution Implementation Roll-out / Production Innovation Lab Bootcamp Solution Design Workshop Solution Audit Staff Augmentation Product Training
  7. 7. 7 Neo4j based Solutions
  8. 8. • Agility -- constantly changing requirements • Intuitiveness – so that everybody in your organization can understand and influence the solution • High Performance to support connected data scenarios • Value Connections • 360 degree views (customer, product, etc.) • Finding patterns, traversing through the data • Sysadmin friendliness • Hardware efficiency8 Technical Requirements (need of Neo4j Graph Based Solution)
  9. 9. 9 From use case to solution delivery Solution accelerators
  10. 10. 10 Why Solutions? Accelerate Customer Success Scale Operations Increased Product Maturity
  11. 11. 11 What is needed? Solution accelerators SOLUTION (FOUNDATION) FRAMEWORK DELIVERY METHODOLOGY SKILLS & RESOURCES
  12. 12. Solution (Foundation) Framework Neo4j Graph Platform Recommendation Framework Custom App Solution Foundation Framework Neo4j Data Orchestrator Framework Neo4j Deployment Framework Neo4j Managed Service Fraud Framework Network Management Framework Custom App Custom App Custom App Custom App Custom App
  13. 13. Solution (Foundation) Framework Neo4j Graph Platform Recom Telco App Solution Foundation Framework Development & Administration Analytics Tooling Graph Analytics Graph Transactions Data Integration Discovery & VisualizationDrivers App App
  14. 14. Solution (Foundation) Framework Neo4j Graph Platform Recom Telco App Solution Foundation Framework Development & Administration Analytics Tooling Graph Analytics Graph Transactions Data Integration Discovery & VisualizationDrivers App App API dev 3rd party graph viz Custom dev with graph viz libraries 3rd party analytics Python, Java ML, ... Kettle 3rd party DI/EAI Docker Kubernetes Git Lineage GRANDstack
  15. 15. 15 Data Integration
  16. 16. GRANDstack https://grandstack.io/ GraphQL API Layer Apollo Client ReactJS ( ReactGraphVis) VisJS Dashboards Business Logic (JS) Apollo Server Neo4j GraphQL Pluginneo4j-graphql-js
  17. 17. Solution (Foundation) Framework Neo4j Graph Platform Recom Telco App Solution Foundation Framework App App Availability Demand Framing & Collateral Sales Demo Solution Beta Repeatable Solution Recommendation Framework HCM Framework Privacy Shield Framework Risk Mgmt Framework Fraud Framework C360 Framework CRM Framework Network Mgmt Bill Of Materials
  18. 18. 18 Recommendation Engine
  19. 19. Network Management (Telco) Graph Transactions Element Manager Geography Service definitions Kettle Customer data ... Dependency Analysis Spatial queries and path exploration Capacity Analysis Fulfillment Assurance > Impact analysis > Event correlation > Root cause analysis Predefined CYPHER queries and API Metadata
  20. 20. 20 What is needed? Solution accelerators SOLUTION (FOUNDATION) FRAMEWORK DELIVERY METHODOLOGY SKILLS & RESOURCES
  21. 21. Main Building BlocksProject definition Solution Design Workshop Deploy Agile Sprints Solution Delivery Methodolgy 21 Product backlog Backlog Product Increment ● Project definition: clarity about objectives and organization ● Solution design workshop: requirements and high level design ● Solution Delivery ○ Agile/SCRUM ○ Traditional / Waterfall ● (Regular) Releases ● Solution support Solution Support Waterfall
  22. 22. 22 What is needed? Solution accelerators SOLUTION (FOUNDATION) FRAMEWORK DELIVERY METHODOLOGY SKILLS & RESOURCES
  23. 23. Architecture and design Roll-out and deploy Project management Operations Management IntegrationAPILogicModel Skills & Methods 23 Database
  24. 24. 24 Managed Solutions Cloud Managed Services Agile Solution Support
  25. 25. 25 Machine Learning and Analytics
  26. 26. Where AI and ML fit in 26 Development & Administration Analytics Tooling Graph Analytics Graph Transactions Data Integration Discovery & VisualizationDrivers & APIs AI
  27. 27. Differences between ML and Analytics 28 Machine learning: • Determine domain parameters • Historical-based discoveries • Learn and improve without explicit programming
  28. 28. Graph analytics: • Uses inherent graph structures • Uncover real-world networks through their connections • Forecast complex network behavior and identify action Differences between ML and Analytics
  29. 29. (Some) today challenges with Machine Learning: • Doesn’t take multiple relationship hops into account • Takes time to iteratively train a model • Computational inefficiency of connecting data Machine Learning Pipeline Benefits of Mixing Graph Analytics with ML Graphs bring: • Context to machine learning • Feature filtration • Connected feature extraction
  30. 30. Neo4j has an ‘out of the box’ Graph Algorithms plugin: • Pathfinding and Search • Centrality and Importance • Community Detection • Similarity and Machine Learning Workflow • Link Prediction Many different ways to work with your ML algorithms in Neo4j: • Support for many languages (Python, .Net, Java, Go, Ruby, etc.) • Different data integration options • Triggers, event-driven architecture, user-defined functions 31 Working with Graph Analytics and ML
  31. 31. Pathfinding & Search Centrality / Importance Community Detection Similarity GraphConnect 2017 GraphConnect 2018 • Minimum Weight Spanning Tree • Shortest Path • Single-Source Shortest Path • All Pairs Shortest Path • PageRank • Article Rank • Betweenness Centrality • Closeness Centrality • Louvain • Label Propagation • Connected Components • Harmonic Centrality • Eigenvector Centrality • Degree Centrality • A* Shortest Path • Yen’s K Shortest Path • Random Walk • Jaccard Similarity • Cosine Similarity • Pearson Similarity • Strongly Connected Components • Triangle Count / • Clustering Coefficient • Balanced Triads Machine Learning and Graph Algorithms in Neo4j • Euclidean Distance • Overlap Similarity Link Prediction • Adamic Adar • Common Neighbors • Preferential Attachment • Resource Allocation • Same Community • Total Neighbors 32 neo4j.com/ graph-algorithms- book/
  32. 32. IoT/Connected Home: • Master Data Management • Entity resolution using community detection and similarity Customer Experience Management: • Customer journey path analysis (path finding) 33 Graph Analytics and Algorithm Examples 33
  33. 33. Knowledge graph example: • Using topic finding ML processes e.g. Latent Dirichlet Allocation (LDA) • Feeding the output into a graph database • Search for topics, find related concepts, etc. 34 Graph and Machine Learning Examples Recommendation engine example: • Use ML processes such as collaborative filtering • Enrich graph with the output • Use graph as feedback for future iterations
  34. 34. Putting it all Together 35
  35. 35. 37 Real World Examples and Best Practices
  36. 36. Customer Use Case: • Leading online platform to showcase and discover creative work • More than 10 million members • Allows creatives to share their work with millions of daily visitors • Highlights Adobe software used in the creation process • Drives people to the Adobe Creative Cloud • Social platform for discovery, learning, and more 38 Adobe – Project Behance Activity feed: • Mongo (2011) - 125 nodes, dataset size of about 20tb (terabytes) • Cassandra (2015) - 48 nodes, dataset size of about 50tb (terabytes) • Neo4j (2018) - 3 nodes, dataset size of 40gb (gigabytes) 5 day Bootcamp
  37. 37. 39 Large Commercial Bank Customer Journey Innovation Lab Staff Augmentation Campaign Management Innovation Lab Fraud Project 80 person days TBD Another Innovation Lab
  38. 38. 40 Conclusion • Neo4j Professional Services makes customer projects successful through: • Enablement • Project / solution delivery • Graph Based Solutions as accelerators • Neo4j is the foundation for AI and ML • Customers are using Neo4j to drive success and deliver value
  39. 39. 41 Thank you
  40. 40. Our Neo4j activity implementation has led to a great decrease in complexity, storage, and infrastructure costs. Our full dataset size is now around 40 GB, down from 50 TB of data that we had stored in Cassandra. We’re able to power our entire activity feed infrastructure using a cluster of 3 Neo4j instances, down from 48 Cassandra instances of pretty much equal specs. That has also led to reduced infrastructure costs. Most importantly, it’s been a breeze for our operations staff to manage since the architecture is simple and lean.” David Fox, Adobe, Oct 2018 42 Customer Quote How can Neo4j Services help you to get there?
  41. 41. 43

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