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#neo4j	
  
New	
  Opportuni0es	
  for	
  
Connected	
  Data	
  
@ianSrobinson	
  
ian@neotechnology.com	
  
	
  
#neo4j	
  
Outline	
  
•  Data	
  complexity	
  
•  Graph	
  databases	
  –	
  features	
  and	
  benefits	
  
•  Querying	
  graph	
  data	
  
•  Use	
  cases	
  
#neo4j	
  
Data	
  Complexity	
  
complexity = f(size, semi-structure, connectedness)
#neo4j	
  
complexity = f(size, semi-structure, connectedness)
Data	
  Complexity	
  
#neo4j	
  
Semi-­‐Structure	
  
#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	
  
#neo4j	
  
Social	
  Network	
  
#neo4j	
  
Network	
  Impact	
  Analysis	
  
#neo4j	
  
Route	
  Finding	
  
#neo4j	
  
Recommenda0ons	
  
#neo4j	
  
Logis0cs	
  
#neo4j	
  
Access	
  Control	
  
#neo4j	
  
Fraud	
  Analysis	
  
#neo4j	
  
Securi0es	
  and	
  Debt	
  
Image:	
  orgnet.com	
  
#neo4j	
  
Graphs	
  Are	
  Everywhere	
  
#neo4j	
  
Graph	
  Databases	
  
•  Store	
  
•  Manage	
  
•  Query	
  
data	
  
#neo4j	
  
Neo4j	
  is	
  a	
  Graph	
  Database	
  
Java	
  APIs	
  
Applica0on	
  
REST	
  API	
  REST	
  API	
  REST	
  API	
  
REST	
  Client	
  
Applica0on	
  
Write	
  LB	
   Read	
  LB	
  
#neo4j	
  
Property	
  Graph	
  Data	
  Model	
  
#neo4j	
  
Nodes	
  
#neo4j	
  
Labels	
  
#neo4j	
  
Rela0onships	
  
#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	
  
#neo4j	
  
Querying	
  Graph	
  Data	
  
•  Describing	
  graphs	
  
•  Crea0ng	
  nodes,	
  rela0onships	
  and	
  proper0es	
  
•  Querying	
  graphs	
  
#neo4j	
  
Describing	
  Graphs	
  
#neo4j	
  
Cypher	
  
(neo4j)<-[:HAS_SKILL]-(ben)-[:HAS_SKILL]->(rest),	
(ben)-[:WORKS_FOR]->(acme)
#neo4j	
  
Cypher	
  
(ben)-[:HAS_SKILL]->(neo4j),	
(ben)-[:HAS_SKILL]->(rest),	
(ben)-[:WORKS_FOR]->(acme)
#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
#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
#neo4j	
  
Node	
  
(ben:person { name:'Ben' })
#neo4j	
  
Iden0fier	
  
(ben:person { name:'Ben' })	
ben	
  =	
  
#neo4j	
  
Label	
  
(ben:person { name:'Ben' })	
ben	
  =	
  
#neo4j	
  
Proper0es	
  
(ben:person { name:'Ben' })	
ben	
  =	
  
#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
#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
#neo4j	
  
Eventually…	
  
#neo4j	
  
Querying	
  a	
  Graph	
  
Graph	
  local:	
  
•  One	
  or	
  more	
  start	
  nodes	
  
•  Explore	
  surrounding	
  graph	
  
•  Millions	
  of	
  joins	
  per	
  second	
  
#neo4j	
  
Pafern	
  Matching	
  
Pafern	
  
#neo4j	
  
Pick	
  Start	
  Node	
  
Pafern	
  
#neo4j	
  
First	
  Match	
  
Pafern	
  
#neo4j	
  
Second	
  Match	
  
Pafern	
  
#neo4j	
  
Third	
  Match	
  
Pafern	
  
#neo4j	
  
Not	
  A	
  Match	
  
Pafern	
  
#neo4j	
  
Not	
  A	
  Match	
  
Pafern	
  
#neo4j	
  
Who	
  Shares	
  My	
  Skill	
  Set?	
  
#neo4j	
  
Colleagues	
  Who	
  Share	
  My	
  Skills	
  
#neo4j	
  
Cypher	
  Pafern	
  
(company)<-[:WORKS_FOR]-(me)	
-[:HAS_SKILL]->(skill),	
(company)<-[:WORKS_FOR]-(colleague)	
-[:HAS_SKILL]->(skill)
#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
#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
#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
#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
#neo4j	
  
Results	
  
+--------------------------------------+	
| name | score | skills |	
+--------------------------------------+	
| "Ben" | 2 | ["Neo4j","REST"] |	
| "Charlie" | 1 | ["Neo4j"] |	
+--------------------------------------+	
2 rows
#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
#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
#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
#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
#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
#neo4j	
  
Results	
  
+---------------------------------------------------+	
| name | score | skills | distance |	
+---------------------------------------------------+	
| "Arnold" | 2 | ["Clojure","Java"] | 2 |	
| "Charlie" | 1 | ["SQL"] | 1 |	
+---------------------------------------------------+	
2 rows
#neo4j	
  
Case	
  Studies	
  
#neo4j	
  
Network	
  Impact	
  Analysis	
  
•  Which	
  parts	
  of	
  network	
  
does	
  a	
  customer	
  
depend	
  on?	
  
•  Who	
  will	
  be	
  affected	
  if	
  
we	
  replace	
  a	
  network	
  
element?	
  
#neo4j	
  
Asset	
  Management	
  &	
  Access	
  Control	
  
•  Which	
  assets	
  can	
  an	
  
admin	
  control?	
  
•  Who	
  can	
  change	
  my	
  
subscrip0on?	
  
#neo4j	
  
Logis0cs	
  
•  What’s	
  the	
  quickest	
  
delivery	
  route	
  for	
  this	
  
parcel?	
  
#neo4j	
  
Social	
  Network	
  &	
  Recommenda0ons	
  
•  Which	
  assets	
  can	
  I	
  
access?	
  
•  Who	
  shares	
  my	
  
interests?	
  
#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	
  
	
  

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