15 minutes brief talk about neo4j and graphdb

1. Neo4j Graph DB Intro & Neo4j DB Fosdem 2011 #neo4j @neo4j [email_address] Jordi Valverde Eclipsi Networks

2. GraphDB evolution Information connectivity 1990 2000 2010 2020 Text documents Ontologies RDF Folksonomies Tagging User-generated content Wikis RSS Blogs Hypertext Neo4j

4. Relationships between nodes

6. Querying Neo4j? (1) GraphDatabaseService graphDb = ... // Get factory // Create Jordi Node jordi = graphDb.createNode(); jordi.setProperty( "name" , "Jordi" ); jordi.setProperty( "age" , 29 ); // Create Car Node car = graphDb.createNode(); car.setProperty( "name" , "Car" ); car.setProperty( "vendor" , "Honda" ); car.setProperty( "model" , "Civic" ); // Create a relationship representing that relationate each element jordi.createRelationshipTo( car, RelTypes. KNOWS );

7. Social data (customer: brand-name social network) 1 3 13 KNOWS KNOWS 7 2 KNOWS KNOWS KNOWS 42 KNOWS name = “Mike” age = 29 disclosure = public name = “Charlie” last_name = “Runkle” name = “Dani” last_name = “California” age = 27 name = “Hank” last_name = “Moody” age = 42 age = 3 days name = “Karen” name = “Marcy Runkle”

8. Just a social graph?

9. Spatial data (customer: large telecom company) 1 3 13 ROAD ROOOAD 7 2 ROAD ROAD ROAD 42 ROAD name = “Omni Hotel” lat = 3492848 long = 283823423 length = 7 miles name = ... lat, long = ... name = “Swedland” lat = 23410349 long = 2342348852 name = “The Tavern” lat = 1295238237 long = 234823492 length = 3 miles name = ... name = ...

10. Social? Spatial? … Social AND spatial!

11. Social AND spatial data 1 3 13 LIKES SIBLING 7 2 ROAD ROAD ROAD 42 KNOWS name = “Omni Hotel” lat = 3492848 long = 283823423 weight = 10 name = “Pere” beer_qual = expert name = “Maria” age = 30 beer_qual = non-existant name = “The Tavern” lat = 1295238237 long = 234823492 length = 3 miles name = ... name = “Jordi”

12. Financial data (customer: international bank) 1 3 13 TRANSFER WITHDRAW 7 2 OWNS DEPOSIT TRANSFER 42 WITHDRAW name = “Mr Godfather” karma = veeeery-low cash = more-than-you amount = $1000 name = “Emil” cash = always-too-li'l title = “ATM @ Wall St” id = 230918484233 cash_left = 384204 name = “The Tavern” lat = 1295238237 long = 234823492 amount = $1000 name = ... name = ...

13. Computational Genomics? (customer: no idea!) 1 3 13 NEXT NEXT 7 2 NUCLEOTIDES FAMILY RELATED 42 START type = “Protein” ID=”SACE0A01650p” name = “T” name = “G” name = “A” name = GL3C2479 name = ...

14. Computational Genomics? (customer: no idea!) 1 3 13 NEXT NEXT 7 2 NUCLEOTIDES FAMILY RELATED 42 START type = “Protein” ID=”SACE0A01650p” name = “T” name = “G” name = “A” name = GL3C2479 name = ...

19. Some examples...

20. Routing

21. Visualization - Gephi

22. Visualization - iGraph

24. Robust: 24/7 production since 2003

25. Mature: lots of production deployments

26. Scalable: High Availability, Master failover

27. Community: ecosystem of tools, bindings, frameworks

32. http://slidesha.re/ftBtb2 Pablo Delgado (ruby on rails and neo4j) Conferencia RoR Madrid

33. http://slidesha.re/heSxmm Pere Urbon (fast peak on graphdb) Barcelona on rails meeting More Info?

34. Questions? Thank you...