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Neo4j GraphTalk Helsinki - Next-Gerneation Telecommunication Solutions with Neo4j

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Jesus Barrasa, Neo4j
GraphTalk Helsinki

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Neo4j GraphTalk Helsinki - Next-Gerneation Telecommunication Solutions with Neo4j

  1. 1. Next-Generation Solutions with Neo4j GraphTalk - Helsinki - 3rd October 2018 Dr. Jesús Barrasa - @BarrasaDV Director Telecom Solutions
  2. 2. Two of the world’s three largest CSPs have chosen to adopt Neo4j to build mission critical solutions.
  3. 3. Half of the 2018 leaders of the Gartner Magic Quadrant for OSS embed Neo4j in their key products.
  4. 4. Two of the Top five US multichannel video service providers have chosen Neo4j to build key customer centric solutions
  5. 5. IoT OSS/BSS Governance & Metadata Mgmnt IAM & Fraud Analysis Common Graph Use Cases in Telco Digital Transformation • Smart Homes • Assurance • Fulfilment • CRM/Support • Planning & Optimisation • Regulatory compliance • Data Lineage • Consent Mgmnt • Identity & Access Mgmnt • CJA / CEM • Customer 360 • Eligibility Engine
  6. 6. why?
  7. 7. Shared Requirements Capture Complexity Allow Flexibility High Performance Bridge Business - IT gap Rich, Dynamic, human friendly Graph Model on a Native Graph Platform
  8. 8. ”Graph analysis is possibly the single most effective competitive differentiator for organisations pursuing data-driven operations and decisions“
  9. 9. Graph Thinking
  10. 10. Audience Experiment: Dependency modelling
  11. 11. Look at this data… Element Depends On A B A C A D C H D J E F E G F J G L H I J N J M L M
  12. 12. Element Depends On A B A C A D C H D J E F E G F J G L H I J N J M L M Time challenge #1: Does A depend on F ? ?
  13. 13. Look at this data again…
  14. 14. Time challenge #2: Does E depend on M ? ? M E
  15. 15. MATCH (a:Element { id: “A”}) MATCH p = (a)-[:DEPENDS_ON*]->(n { id: “N”})
 RETURN p SELECT d1.ElementId, d2.ElementId, d3.ElementId 
 FROM dpndncs AS d1 INNER JOIN dpndncs AS d2 ON d1.dependsOnElemId = d2.ElemId INNER JOIN dpndncs AS d3 ON d2.dependsOnElemId = d3.ElemId … <arbitrary number of joins>…
 WHERE d1.ElementId = “A” AND d3.ElementId = “N” Element Depends On A B A C A D C H D J E F E G F J G L H I J N J M L M Does X depend on Y ?
  16. 16. Is X affected by a failure in Y? Does X depend on Y ? =
  17. 17. Things get more complicated: Detecting SPOFs
  18. 18. Detect the SPOF for Element E
  19. 19. (spof)<-[:DEPENDS_ON*]-(x:Element)-[:DEPENDS_ON*]->(spof) Detect the SPOF on a graph?
  20. 20. SPOF on tables anyone ? ElemId dependsOnElemId A B A C A D C H D J E F E G F J G L H I J N J M L M
  21. 21. Service Assurance Traffic Engineering
  22. 22. We have a complex multilayer network and we have two problems: 1. Design + Planning 2. Fault Management Build me a graph!
  23. 23. Route oriented Model Shortest/most efficient path from A to B Find diverse routes between A and B
  24. 24. Route oriented Model
  25. 25. Dependency oriented Model
  26. 26. Dependency oriented Model
  27. 27. Dual Model
  28. 28. Neo4j Bloom Custom Viz Third party BI tool Neo4j Browser
  29. 29. Traffic Engineering Diverse Routing ?
  30. 30. Traffic Engineering Diverse Routing Path Analysis Least cost path from A to B + Dependency Analysis No shared underlying resources
  31. 31. Network Planning Example
  32. 32. CALL spatial.closest('l1', $origin , 0.3) YIELD node AS oN CALL spatial.closest(‘l1', $dest , 0.3) YIELD node AS dN MATCH shortest = shortestPath((oN)-[r:LINK*..5]-(dN)) RETURN * Combining Geospatial with path exploration
  33. 33. CALL spatial.closest('l1', $origin , 0.3) YIELD node AS oN CALL spatial.closest(‘l1', $dest , 0.3) YIELD node AS dN MATCH shortest = shortestPath((oN)-[r:LINK*..5]-(dN)) RETURN * Combining Geospatial with path exploration
  34. 34. CALL spatial.closest('l1', $origin , 0.3) YIELD node AS oN CALL spatial.closest(‘l1', $dest , 0.3) YIELD node AS dN MATCH shortest = shortestPath((oN)-[r:LINK*..5]-(dN)) RETURN * Combining Geospatial with path exploration
  35. 35. CALL spatial.closest('l1', $origin , 0.3) YIELD node AS oN CALL spatial.closest(‘l1', $dest , 0.3) YIELD node AS dN MATCH shortest = shortestPath((oN)-[r:LINK*..5]-(dN)) RETURN * Combining Geospatial with path exploration
  36. 36. 🏦 :DEPENDS_ON :DEPENDS_ON :DEPENDS_ON IF/AX2431 💥 Customer Event Correlation Event Prioritisation Fault Management (Deep) Impact/Root Cause Analysis
  37. 37. MATCH (fe:Link { linkId: $id})<-[:CARRIED_BY*]-(s:Service) RETURN max(s.priority) AS severity (Deep) Impact/Root Cause Analysis { alarmType: “LOS”, notifyingEntity: “IF/AX/0/3”, …}
  38. 38. Graph Size: ~50M nodes (avg depth: 6)Graph Size: ~1K nodes (avg depth: 5) Simulation: 128 clients, synchronous requests with1ms wait between requests AT SCALE 50000x increase in size of dataset -> 1.14x impact in query performance Graph Native Matters!!! (Deep) Impact/Root Cause Analysis
  39. 39. Customer Journey Analysis
  40. 40. What is CJA? 4
  41. 41. Why is CJA important? Because customers… have an improved opinion of businesses that remember previous interactions with them value personalised and relevant information
  42. 42. Why is CJA important? Because organisations… want to give managers an overview of the customer’s experience want to uncover and solve gaps between channels, departments, devices... bringing significant business value
  43. 43. Three recurring concepts: CUSTOMER TIME MULTICHANNEL
  44. 44. The tool: Customer Journey Maps
  45. 45. “ Journey maps are based largely on the assumption that customer experience is unchanging and controllable, and thus can be captured as a standardised process. But a customer’s journey isn’t a simple and finite series of steps: it’s a complex and sometimes contradictory set of interactions over multiple channels. Map that! ” Jonathan Browne. Forrester Research
  46. 46. ActiveCJM on Neo4j Visualisable Flexible & Dynamic Agile Actionable Identification of key milestones in relevant journeys JOURNEY DISCOVERY/DESIGN Identifying & connecting relevant data sources DATA COLLECTION Construction of automated DD integrations. -> Next best action, Notifications, Recommendations, etc BUILD AUTOMATED INTERACTIONS
  47. 47. Demo
  48. 48. CRM Service Usage Helpdesk Incidents & service perf. Marketing Automated Processing Analysis Active Predictive Model Recommendations Next Best Accion Active CJM
  49. 49. Conclusions
  50. 50. Graph thinking : Rethink your problem as a graph Graph: Efficient / flexible / expressive representation of your data Graph Native Matters! Neo4j platform gives you a head start
  51. 51. Kiitos!

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