Today's complex data is not only big, but also semi-structured and densely connected. In this session we'll look at how size, structure and connectedness have converged to change the way we work with data. We'll then go on to look at some of the new opportunities for creating end-user value that have emerged in a world of connected data, illustrated with practical examples implemented using Neo4j, the world's leading graph database. Speaker: Ian Robinson, Director of Customer Success, Neo Technology Ian is an engineer at Neo Technology, currently working on research and development for future versions of the Neo4j graph database. Prior to joining the engineering team, Ian served as Neo's Director of Customer Success, managing training, professional services and support, and working with customers to design and develop graph database solutions. He is a co-author of 'Graph Databases' (O'Reilly) and 'REST in Practice' (O'Reilly), and a contributor to 'REST: From Research to Practice' (Springer) and 'Service Design Patterns' (Addison-Wesley). He presents at conferences worldwide on REST and the graph capabilities of Neo4j, and blogs at http://iansrobinson.com.

1. #neo4j
New
Opportuni0es
for
Connected
Data
@ianSrobinson
ian@neotechnology.com

2. #neo4j
Outline
• Data
complexity
• Graph
databases
–
features
and
benefits
• Querying
graph
data
• Use
cases

3. #neo4j
Data
Complexity
complexity = f(size, semi-structure, connectedness)

4. #neo4j
complexity = f(size, semi-structure, connectedness)
Data
Complexity

5. #neo4j
Semi-­‐Structure

6. #neo4j
Semi-­‐Structure
Email:
ian@neotechnology.com
Email:
iansrobinson@gmail.com
Twi*er:
@iansrobinson
Skype:
iansrobinson
FIRST_NAME
LAST_NAME
USER_ID
EMAIL_1
EMAIL_2
TWITTER
FACEBOOK
SKYPE
Ian
Robinson
315
ian@neotechnology.com
iansrobinson@gmail.com
@iansrobinson
NULL
iansrobinson
USER
CONTACT
CONTACT_TYPE
0..n

7. #neo4j
Social
Network

8. #neo4j
Network
Impact
Analysis

9. #neo4j
Route
Finding

10. #neo4j
Recommenda0ons

11. #neo4j
Logis0cs

12. #neo4j
Access
Control

13. #neo4j
Fraud
Analysis

14. #neo4j
Securi0es
and
Debt
Image:
orgnet.com

15. #neo4j
Graphs
Are
Everywhere

16. #neo4j
Graph
Databases
• Store
• Manage
• Query
data

17. #neo4j
Neo4j
is
a
Graph
Database
Java
APIs
Applica0on
REST
API
REST
API
REST
API
REST
Client
Applica0on
Write
LB
Read
LB

18. #neo4j
Property
Graph
Data
Model

19. #neo4j
Nodes

20. #neo4j
Labels

21. #neo4j
Rela0onships

22. #neo4j
Graph
Database
Benefits
“Minutes
to
milliseconds”
performance
• Millions
of
‘joins’
per
second
• Consistent
query
0mes
as
dataset
grows
Fit
for
the
domain
• Lots
of
join
tables?
Connectedness
• Lots
of
sparse
tables?
Semi-­‐structure
Business
responsiveness
• Easy
to
evolve

23. #neo4j
Querying
Graph
Data
• Describing
graphs
• Crea0ng
nodes,
rela0onships
and
proper0es
• Querying
graphs

24. #neo4j
Describing
Graphs

25. #neo4j
Cypher
(neo4j)<-[:HAS_SKILL]-(ben)-[:HAS_SKILL]->(rest),
(ben)-[:WORKS_FOR]->(acme)

26. #neo4j
Cypher
(ben)-[:HAS_SKILL]->(neo4j),
(ben)-[:HAS_SKILL]->(rest),
(ben)-[:WORKS_FOR]->(acme)

27. #neo4j
Create
Some
Data
CREATE (ben:person { name:'Ben' }),
(acme:company { name:'Acme' }),
(rest:skill { name:'REST' }),
(neo4j:skill= { name:'Neo4j' }),
(ben)-[:WORKS_FOR]->(acme),
(ben)-[:HAS_SKILL]->(rest),
(ben)-[:HAS_SKILL]->(graphs)
RETURN ben

28. #neo4j
Create
Nodes
CREATE (ben:person { name:'Ben' }),
(acme:company { name:'Acme' }),
(rest:skill { name:'REST' }),
(neo4j:skill= { name:'Neo4j' }),
(ben)-[:WORKS_FOR]->(acme),
(ben)-[:HAS_SKILL]->(rest),
(ben)-[:HAS_SKILL]->(graphs)
RETURN ben

29. #neo4j
Node
(ben:person { name:'Ben' })

30. #neo4j
Iden0fier
(ben:person { name:'Ben' })
ben
=

31. #neo4j
Label
(ben:person { name:'Ben' })
ben
=

32. #neo4j
Proper0es
(ben:person { name:'Ben' })
ben
=

33. #neo4j
Create
Rela0onships
CREATE (ben:person { name:'Ben' }),
(acme:company { name:'Acme' }),
(rest:skill { name:'REST' }),
(neo4j:skill= { name:'Neo4j' }),
(ben)-[:WORKS_FOR]->(acme),
(ben)-[:HAS_SKILL]->(rest),
(ben)-[:HAS_SKILL]->(graphs)
RETURN ben

34. #neo4j
Return
Node
CREATE (ben:person { name:'Ben' }),
(acme:company { name:'Acme' }),
(rest:skill { name:'REST' }),
(neo4j:skill= { name:'Neo4j' }),
(ben)-[:WORKS_FOR]->(acme),
(ben)-[:HAS_SKILL]->(rest),
(ben)-[:HAS_SKILL]->(graphs)
RETURN ben

35. #neo4j
Eventually…

36. #neo4j
Querying
a
Graph
Graph
local:
• One
or
more
start
nodes
• Explore
surrounding
graph
• Millions
of
joins
per
second

37. #neo4j
Pafern
Matching
Pafern

38. #neo4j
Pick
Start
Node
Pafern

39. #neo4j
First
Match
Pafern

40. #neo4j
Second
Match
Pafern

41. #neo4j
Third
Match
Pafern

42. #neo4j
Not
A
Match
Pafern

43. #neo4j
Not
A
Match
Pafern

44. #neo4j
Who
Shares
My
Skill
Set?

45. #neo4j
Colleagues
Who
Share
My
Skills

46. #neo4j
Cypher
Pafern
(company)<-[:WORKS_FOR]-(me)
-[:HAS_SKILL]->(skill),
(company)<-[:WORKS_FOR]-(colleague)
-[:HAS_SKILL]->(skill)

47. #neo4j
Find
Colleagues
MATCH (company)<-[:WORKS_FOR]-(me:person)
-[:HAS_SKILL]->(skill),
(company)<-[:WORKS_FOR]-(colleague)
-[:HAS_SKILL]->(skill)
WHERE me.name = 'Ian'
RETURN colleague.name AS name,
count(skill) AS score,
collect(skill.name) AS skills
ORDER BY score DESC

48. #neo4j
Find
Colleagues
MATCH (company)<-[:WORKS_FOR]-(me:person)
-[:HAS_SKILL]->(skill),
(company)<-[:WORKS_FOR]-(colleague)
-[:HAS_SKILL]->(skill)
WHERE me.name = 'Ian'
RETURN colleague.name AS name,
count(skill) AS score,
collect(skill.name) AS skills
ORDER BY score DESC

49. #neo4j
Find
Colleagues
MATCH (company)<-[:WORKS_FOR]-(me:person)
-[:HAS_SKILL]->(skill),
(company)<-[:WORKS_FOR]-(colleague)
-[:HAS_SKILL]->(skill)
WHERE me.name = 'Ian'
RETURN colleague.name AS name,
count(skill) AS score,
collect(skill.name) AS skills
ORDER BY score DESC

50. #neo4j
Find
Colleagues
MATCH (company)<-[:WORKS_FOR]-(me:person)
-[:HAS_SKILL]->(skill),
(company)<-[:WORKS_FOR]-(colleague)
-[:HAS_SKILL]->(skill)
WHERE me.name = 'Ian'
RETURN colleague.name AS name,
count(skill) AS score,
collect(skill.name) AS skills
ORDER BY score DESC

51. #neo4j
Results
+--------------------------------------+
| name | score | skills |
+--------------------------------------+
| "Ben" | 2 | ["Neo4j","REST"] |
| "Charlie" | 1 | ["Neo4j"] |
+--------------------------------------+
2 rows

52. #neo4j
Search
the
En0re
Network
MATCH (me:person)-[:HAS_SKILL]->(skill),
(company)<-[:WORKS_FOR]-(person)
-[:HAS_SKILL]->(skill)
WHERE me.name = 'Ian'
RETURN person.name AS name,
company.name AS company,
count(skill) AS score,
collect(skill.name) AS skills
ORDER BY score DESC

53. #neo4j
Search
the
En0re
Network
MATCH (me:person)-[:HAS_SKILL]->(skill),
(company)<-[:WORKS_FOR]-(person)
-[:HAS_SKILL]->(skill)
WHERE me.name = 'Ian'
RETURN person.name AS name,
company.name AS company,
count(skill) AS score,
collect(skill.name) AS skills
ORDER BY score DESC

54. #neo4j
Results
+--------------------------------------------------------------+
| name | company | score | skills |
+--------------------------------------------------------------+
| "Arnold" | "Startup, Ltd" | 3 | ["Java","Neo4j","REST"] |
| "Ben" | "Acme, Inc" | 2 | ["Neo4j","REST"] |
| "Gordon" | "Startup, Ltd" | 1 | ["Neo4j"] |
| "Charlie" | "Acme, Inc" | 1 | ["Neo4j"] |
+--------------------------------------------------------------+
4 rows

55. #neo4j
Find
People
With
Matching
Skills
MATCH p=(me:person)-[:WORKED_ON*2..4]-(person)
-[:HAS_SKILL]->(skill)
WHERE me.name = 'Ian'
AND person <> me
AND skill.name IN ['Java','Clojure','SQL']
WITH person, skill, min(length(p)) as pathLength
RETURN person.name AS name,
count(skill) AS score,
collect(skill.name) AS skills,
((pathLength - 1)/2) AS distance
ORDER BY score DESC

56. #neo4j
Find
People
With
Matching
Skills
MATCH p=(me:person)-[:WORKED_ON*2..4]-(person)
-[:HAS_SKILL]->(skill)
WHERE me.name = 'Ian'
AND person <> me
AND skill.name IN ['Java','Clojure','SQL']
WITH person, skill, min(length(p)) as pathLength
RETURN person.name AS name,
count(skill) AS score,
collect(skill.name) AS skills,
((pathLength - 1)/2) AS distance
ORDER BY score DESC

57. #neo4j
Results
+---------------------------------------------------+
| name | score | skills | distance |
+---------------------------------------------------+
| "Arnold" | 2 | ["Clojure","Java"] | 2 |
| "Charlie" | 1 | ["SQL"] | 1 |
+---------------------------------------------------+
2 rows

58. #neo4j
Case
Studies

59. #neo4j
Network
Impact
Analysis
• Which
parts
of
network
does
a
customer
depend
on?
• Who
will
be
affected
if
we
replace
a
network
element?

60. #neo4j
Asset
Management
&
Access
Control
• Which
assets
can
an
admin
control?
• Who
can
change
my
subscrip0on?

61. #neo4j
Logis0cs
• What’s
the
quickest
delivery
route
for
this
parcel?

62. #neo4j
Social
Network
&
Recommenda0ons
• Which
assets
can
I
access?
• Who
shares
my
interests?

63. #neo4j
Download
the
free
book
from
O’Reilly
hfp://graphdatabases.com
Ian Robinson,
Jim Webber & Emil Eifrem
Graph
Databases
h
Com
plim
ents
ofNeo
Technology
Thank
you
@ianSrobinson
ian@neotechnology.com
github.com/iansrobinson