Next Generation Solutions with Neo4j

1. Next-Generation Solutions with Neo4j GraphTour - Toronto - 3rd May 2018 Dr. Jesús Barrasa - Director Telecoms @Neo4j

2. The answer is GRAPHS… but what was the question?

3. Capture Complexity Allow Flexibility Performance for Automated processing Bridge the gap between business and IT Rich Graph Model Dynamic Graph Model Graph Native Storage Humane (intuitive) Model

4. Solution: Fulﬁlment & Assurance in Telco

5. Audience Experiment: Dependency modelling

6. 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

7. 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 ? ?

8. Look at this data again…

9. Time challenge #2: Does E depend on M ? ? M E

10. 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 ?

11. Is X affected by a failure in Y? Does X depend on Y ? =

12. Things get more complicated: Detecting SPOFs

13. Detect the SPOF for Element E

14. (spof)<-[:DEPENDS_ON*]-(x:Element)-[:DEPENDS_ON*]->(spof) Detect the SPOF on a graph?

15. 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

16. We have a complex multilayer network and we have two problems: 1. Design + Planning 2. Impact Analysis Build me a graph!

17. Route oriented Model Shortest/most eﬃcient path from A to B Find diverse routes between A and B

18. Route oriented Model

19. Dependency oriented Model

20. Dependency oriented Model

21. Dual Model

22. Network Planning Example

23. 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

28. (Deep) Impact/Root Cause Analysis 🏦 :DEPENDS_ON :DEPENDS_ON :DEPENDS_ON IF/AX2431 💥 Customer Event Correlation Event Prioritisation

29. 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”, …}

30. 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

31. Solution: Fraud Analysis

32. Card fraud origination and assessment of potential impact

34. Mark Robert Sheila Kate

37. Data volume: 518 card payments were made every second last year by cardholders both in the UK and travelling overseas (*) (*) 2017 Stats. The UK Cards Association: http://www.theukcardsassociation.org.uk/

38. WITH { amount: 2.50, currency:"USD", txid:"05015244006", mid:"5073047", tid:"5073440-7", timestamp:1490060618007, cardno:"5224654370862586050" } AS newTxData MATCH (lastTx:Transaction { cardno: newTxData.cardno }) WHERE NOT (lastTx)-[:NEXT]->() CREATE (newTx:Transaction) SET newTx += newTxData CREATE (lastTx)-[:NEXT]->(newTx) WITH newTx, newTxData MERGE (term:Terminal { tid: newTxData.tid}) CREATE (newTx)-[:IN_TERMINAL]->(term) Tx Tx Tx Tx Fraud Fraud Data load: Transactions

43. WITH { txid:"0501524400006"} AS unrecognizedTx MATCH (tx:Transaction { txid: unrecognizedTx.txid }) SET tx:FraudTx Tx Tx Tx Tx Fraud Fraud Data load: Reported fraud

48. MATCH (term:Terminal)<-[:IN_TERMINAL]-(t)-[n:NEXT*]->(:FraudTx) WITH term , count(distinct t.cardno) as ct, min(t.timestamp) as mindate, max(t.timestamp) as maxdate WHERE ct > 1 MATCH (term)<-[:IN_TERMINAL]-(otherTx) WHERE otherTx.timestamp < maxdate and otherTx.timestamp > mindate RETURN term.tid AS terminal,mindate,maxdate, 100 * ct / COUNT(DISTINCT otherTx.cardno) AS impact, (maxdate - mindate)/(24*3600000) as timewindow ORDER BY impact DESC, timewindow DESC Query: Fraud origination at terminal level

52. Query: Fraud origination at terminal level

53. WITH { tid : '2373743-7', from: 1487340089000, to: 1488039852000 } AS compTerm MATCH (term:Terminal { tid: compTerm.tid} )<-[:IN_TERMINAL]-(t) WHERE NOT (t)-[:NEXT*]->(:FraudTx) AND t.timestamp > compTerm.from AND t.timestamp < compTerm.to RETURN distinct t.cardno AS cardAtRisk Query: Proactive prevention

56. Query: Proactive prevention

57. Why graph native matters DB#1 1027910 nodes 4017217 relationships 10044420 properties DB#2 509451186 nodes 1008977685 relationships 3551517114 properties Fraud origination at terminal level 93ms 104 ms Fraud origination at merchant level 102ms 116 ms Proactive prevention 11ms 12 ms

58. Conclusions

59. Graph thinking : Rethink your problem as a graph Graph Native Matters Graphs rock! Enjoy the rest of the day

60. Thanks! Questions?

Next Generation Solutions with Neo4j

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