The document provides an agenda and overview of a presentation about graph-based solutions using Neo4j for telecom services. The presentation covers topics like service assurance in telecoms using Neo4j to model dependencies, performing impact and root cause analysis, and using graphs for governance, metadata management and GDPR compliance through concepts like data modeling, transformation, and entitlement management. The conclusions emphasize that graphs are well-suited for these domains and that the combination of domain expertise with Neo4j's graph capabilities provides powerful solutions.

1. Next-Generation Solutions
with Neo4j
GraphTour - Madrid - 15 Feb 2018
Jesús Barrasa - Director Telecoms

2. Agenda
Graph Based Solutions with Neo4j
Service Assurance in Telecoms
Governance, Metadata Mngmnt, GDPR
Conclusions

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. Service Assurance

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. Is X affected by a failure in Y?
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. Detecting SPOFs

12. Detect the SPOF for Element E

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

14. 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

15. Which element in your network needs
additional redundancy?
Is there an element in your network that can
explain the failure of X,Y and Z?

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

17. Route oriented Model

18. Dependency oriented Model

19. Dependency oriented Model

20. Dual Model

22. Neo4j Browser
Custom Viz
Third party BI tool

23. Network Planning Example

24. 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

25. 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

26. 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

27. 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

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

30. MATCH (origin:Link { linkId: $id})<-[:CARRIED_BY*]-(s:Service)
RETURN max(s.priority) AS severity
(Deep) Impact/Root Cause Analysis

31. 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

32. Governance, Metadata
Management and GDPR

33. Look at this dataset

35. Look at the dataset again

37. Data Modelling and Definition
1
Data Transformation2
Consent Management 3
Entitlement 4
GRAPHS IN
SUPPORT OF
GDPR
COMPLIANCE

38. #1 Data Modelling and
Definition

39. Party
CUST_SCHEMA
Party
First NameHAS_LOGICAL_ATT
CUST_SCH
EMA.ROLE
CUST_SCHEM
A_PARTY.FIRST_
NM
CUST_SCH
EMA.PARTY
SHEMA_HAS_TABLE
#1 Data Modelling and Definition
Party
Last Name
HAS_LOGICAL_ATT
SHEMA_HAS_TABLE
GENERATES
CONCEPT_FOR
CONCEPT_FOR
CONCEPT_FOR
HAS_NAME
CUSTOMER
NAMECUSTOMER
HAS_PHONE
TABLE_HAS_COL
CUST_SCHEM
A_PARTY.LAST_
NM
TABLE_HAS_COL
GENERATES
Enterprise Ontology
Application Logical
Model
Physical Schema

40. m
#2 Data Transformation

41. ETL_PROC
_1
SALES_SCHEMA
Normaliz
e_Date
SLS_SCHEM
A.PRODUCT
SLS_SCHEMA.S
ALES.DATE
SLS_SCHE
MA.SALES
SHEMA_HAS_TABLE
#2 Data Transformation
Channel_No
rmalization
SHEMA_HAS_TABLE
HAS_COL
SLS_SCHEMA.S
ALES.CHANNELHAS_COL
BusinessView
Integration MiddlewareOperational Systems
HAS_INPUT
HAS_INPUT
Time.time_key
Time.day_of_
week
Enterprise DWH
HAS_OUTPUT
HAS_OUTPUT
HAS_COL
HAS_COL
TechnicalView
Billing
System
EDWH
CDE:
Transaction
_Date
SENDS RECEIVES
CONCEPTUAL_ELEMENT_FOR
Star_Schema
Star_Sch
ema.Time

42. #3 Consent management

43. #3 Consent Management + MDM
2 Blackfriars
Bridge rd.
J.Barrasa
+44776611…
jesus@neo4j
.com
PHO
NE_FO
R
EMAIL_FOR
ADDRESS_FOR
jb@outlook.
com
EMAIL_FOR
{ contrib: ‘XYZ’,
permittedFor: [UC1,UC4],
consentUntil : 31-12-19 }
{ contrib: ‘internal’,
permittedFor: [UC3],
consentUntil : 31-12-20 }
{ contrib: ‘internal’,
permittedFor: [UC3],
consentUntil : 31-12-20 }
{ contrib: ‘internal’,
permittedFor: [UC3],
consentUntil : 31-12-20 }
EMAIL_FOR
{ contrib: ‘LMN’,
permittedFor: [UC2,UC6],
consentUntil : 31-12-20 }

44. #4 Entitlement

45. #4 Entitlement
User 1 User 3User 2
Exclusion
List G1
Resource 1
Group 1
Resource 2
Group 3
D
MEMBER
CRUD
MEMBER
R
M
EM
BER
MEMBER
CRUD

48. Big 4 Bank in UK: Using Neo4j for Metadata Lineage
for GDPR

49. • GDPR: Protecting,
Categorising and Auditing
personal data.
• Lineage
• Track Changes
• Impact analysis
APM +
ITOA
Graph
Database
APM +
ITOA
Data
Classification

50. Conclusions (I)
Graphs are everywhere - Use graph thinking
Graph Native Matters

51. Conclusions (II)
Combination of
domain skills with
Neo4j best practices
is a winner!
Neo4j Platform
Gr aph Tr ansact ions Gr aph Analyt ics
Data Integration
Developm ent &
Adm in Analyt ics Tooling
Drivers & API s Discovery & Visualizat ion
Developer s
Adm ins
Applicat ions Business User s
Dat a Analyst s
Dat a Scient ist s
3rd Party Tools
“The Graph Advantage”
Domain know-how Professional Services PS Packages
Graph Based Solution

52. Conclusions (III)
Graphs rock!
Enjoy the rest of the day

53. Thank you. Questions?

54. Next-Generation Solutions
with Neo4j
GraphTour - Madrid - 15 Feb 2018
Jesús Barrasa - Director Telecoms