The document discusses new opportunities for connected data using graph databases. It covers topics like data complexity, graph databases features and benefits, querying graph data, and use cases. The key points are that graph databases are well-suited for domains with dense connections and semi-structured data, allow for easy data model evolution, and enable fast querying of relationships between millions of entities.

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

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

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

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

5. Semi-­‐Structure
#neo4j

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

7. Connectedness
#neo4j

8. #neo4j

9. #neo4j

10. #neo4j

11. #neo4j

12. #neo4j

13. #neo4j

14. #neo4j

15. Graphs
Are
Everywhere
#neo4j

16. Neo4j
is
a
Graph
Database
• Store
• Manage
• Query
data
as
a
graph
#neo4j

17. Property
Graph
Data
Model
#neo4j

18. Graph
Database
Benefits
Densely-­‐connected,
semi-­‐structured
domains
• Lots
of
join
tables?
Connectedness
• Lots
of
sparse
tables?
Semi-­‐structure
Data
Model
Vola=lity
• Easy
to
evolve
Join
Complexity
and
Performance
• Millions
of
‘joins’
per
second
• Consistent
query
0mes
as
dataset
grows
#neo4j

19. Querying
Graph
Data
• A
language
for
describing
graphs
• Crea0ng
nodes,
rela0onships
and
proper0es
• Querying
data
#neo4j

20. Describing
Graphs
#neo4j

21. Cypher
(graphs)<-[:INTERESTED_IN]-(ben)-[:INTERESTED_IN]->(rest),
(ben)-[:WORKS_FOR]->(acme)
#neo4j

22. Cypher
(ben)-[:INTERESTED_IN]->(graphs),
(ben)-[:INTERESTED_IN]->(rest),
(ben)-[:WORKS_FOR]->(acme)
#neo4j

23. Create
Some
Data
CREATE ben = { name : 'Ben' },
acme = { name : 'Acme' },
rest = { label : 'REST' },
graphs = { label : 'Graphs' },
ben-[:WORKS_FOR]->acme,
ben-[:INTERESTED_IN]->rest,
ben-[:INTERESTED_IN]->graphs
RETURN ben
#neo4j

24. Create
Nodes
CREATE ben = { name : 'Ben' },
acme = { name : 'Acme' },
rest = { label : 'REST' },
graphs = { label : 'Graphs' },
ben-[:WORKS_FOR]->acme,
ben-[:INTERESTED_IN]->rest,
ben-[:INTERESTED_IN]->graphs
RETURN ben
#neo4j

25. Create
Rela0onships
CREATE ben = { name : 'Ben' },
acme = { name : 'Acme' },
rest = { label : 'REST' },
graphs = { label : 'Graphs' },
ben-[:WORKS_FOR]->acme,
ben-[:INTERESTED_IN]->rest,
ben-[:INTERESTED_IN]->graphs
RETURN ben
#neo4j

26. Return
Node
CREATE ben = { name : 'Ben' },
acme = { name : 'Acme' },
rest = { label : 'REST' },
graphs = { label : 'Graphs' },
ben-[:WORKS_FOR]->acme,
ben-[:INTERESTED_IN]->rest,
ben-[:INTERESTED_IN]->graphs
RETURN ben
#neo4j

27. Eventually…
#neo4j

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

29. Queries:
Pacern
Matching
Pacern
#neo4j

30. Start
Node
Pacern
#neo4j

31. Match
Pacern
#neo4j

32. Match
Pacern
#neo4j

33. Match
Pacern
#neo4j

34. Non-­‐Match
Pacern
#neo4j

35. Non-­‐Match
Pacern
#neo4j

36. Colleagues
Who
Share
My
Interests
#neo4j

37. Cypher
Pacern
me-[:INTERESTED_IN]->topic,
me-[:WORKS_FOR]->company,
colleague-[:INTERESTED_IN]->topic,
colleague-[:WORKS_FOR]->company
#neo4j

38. Find
Colleagues
START me=node:users(name = "sarah")
MATCH me-[:INTERESTED_IN]->topic,
me-[:WORKS_FOR]->company,
colleague-[:INTERESTED_IN]->topic,
colleague-[:WORKS_FOR]->company
RETURN colleague.name AS name,
count(topic) AS score,
collect(topic.label) AS interests
ORDER BY score DESC
#neo4j

39. Find
Start
Node
START me=node:users(name = "sarah")
MATCH me-[:INTERESTED_IN]->topic,
me-[:WORKS_FOR]->company,
colleague-[:INTERESTED_IN]->topic,
colleague-[:WORKS_FOR]->company
RETURN colleague.name AS name,
count(topic) AS score,
collect(topic.label) AS interests
ORDER BY score DESC
#neo4j

40. Describe
Pacern
START me=node:users(name = "sarah")
MATCH me-[:INTERESTED_IN]->topic,
me-[:WORKS_FOR]->company,
colleague-[:INTERESTED_IN]->topic,
colleague-[:WORKS_FOR]->company
RETURN colleague.name AS name,
count(topic) AS score,
collect(topic.label) AS interests
ORDER BY score DESC
#neo4j

41. Construct
Results
START me=node:users(name = "sarah")
MATCH me-[:INTERESTED_IN]->topic,
me-[:WORKS_FOR]->company,
colleague-[:INTERESTED_IN]->topic,
colleague-[:WORKS_FOR]->company
RETURN colleague.name AS name,
count(topic) AS score,
collect(topic.label) AS interests
ORDER BY score DESC
#neo4j

42. Case
Studies
#neo4j

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

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

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

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

47. Thank
You
@iansrobinson
ian.robinson@neotechnology.com
#neo4j

48. Graph
Databases
na0ve
Graph
Processing
FlockDB AllegroGraph
non-­‐na0ve
Graph
Storage
na0ve
#neo4j

49. RDF
Triples
#neo4j

50. Hypergraph
#neo4j