Transformer vos solutions Telco avec Neo4j
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1) Two of the world's three largest telecommunications companies have chosen to adopt Neo4j to build critical solutions. 2) Half of the 2018 leaders in Gartner's Magic Quadrant for operations support systems embed Neo4j in their key products. 3) Two of the top five U.S. multi-channel video service providers have selected Neo4j to develop important customer-focused solutions.
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Transformer vos solutions Telco avec Neo4j
- 1. Transformer vos solutions Telco avec Neo4j Dr. Jesús Barrasa - @BarrasaDV Director Telco&Media - Neo4j Paris - 11 Mars 2019 STAND A40
- 8. Neo4j in the Telco industry
- 9. Two of the world’s three largest CSPs have chosen to adopt Neo4j to build mission critical solutions.
- 10. Half of the 2018 leaders of the Gartner Magic Quadrant for OSS embed Neo4j in their key products.
- 11. Two of the Top five US multichannel video service providers have chosen Neo4j to build key customer centric solutions
- 12. IoT OSS Governance & Metadata Mgmnt Common Graph Use Cases in Telco CX • RA: Fraud • Unified catalog • CRM/Support • IAM • Compliance • Data Lineage • Consent Mgmnt • CJA / CEM • Customer 360 • Eligibility Engine • Smart Home • Smart Cities • Industrial / Manufacturing BSS • Assurance • Unified Inventory • Traffic Engineering • Planning & Optimisation
- 13. pourquoi?
- 14. Shared Requirements Capture Complexity Allow Flexibility High Performance Bridge Business - IT gap Rich, Dynamic, human friendly Graph Model on a Native Graph Platform
- 15. ”Graph analysis is possibly the single most effective competitive differentiator for organisations pursuing data-driven operations and decisions“
- 16. Graph Thinking
- 18. 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
- 19. 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 ? ?
- 20. Look at this data again…
- 21. Time challenge #2: Does E depend on M ? ? M E
- 22. 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 ?
- 23. Is X affected by a failure in Y? Does X depend on Y ? =
- 24. Things get more complicated: Detecting SPOFs
- 25. Detect the SPOF for Element E
- 27. 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
- 29. We have a complex multilayer network and we have two problems: 1. Design + Planning 2. Fault Management Build me a graph!
- 30. Route oriented Model Shortest/most efficient path from A to B Find diverse routes between A and B
- 34. Dual Model
- 37. Neo4j Bloom Custom Viz Third party BI tool Neo4j Browser
- 39. Traffic Engineering Diverse Routing Path Analysis Least cost path from A to B + Dependency Analysis No shared underlying resources
- 40. 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
- 41. CALL spatial.closest('l1', $origin , 0.3) YIELD node AS 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
- 42. CALL spatial.closest('l1', $origin , 0.3) YIELD node AS oN CALL spatial.closest(‘l1', $dest , 0.3) YIELD node AS MATCH shortest = shortestPath((oN)-[r:LINK*..5]-(dN)) RETURN * Combining Geospatial with path exploration
- 43. 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
- 45. 🏦 :DEPENDS_ON :DEPENDS_ON :DEPENDS_ON IF/AX2431 💥 Customer Event Correlation Event Prioritisation Fault Management (Deep) Impact/Root Cause Analysis
- 46. 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”, …}
- 47. 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
- 48. Conclusions
- 49. 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 https://neo4j.com/download/
- 52. Merci!