An introduction to Titan and TinkerPop. http://www.meetup.com/nj-datascience/events/222637527/

1. Text
Titan
By Isaac Rieksts
@IsaacRieksts
1These thoughts are mine own and do not represent the company

2. Outline
Graph Database overview
Tinkerpop
Titan
Graph Queries
Our use of Ttian
Demo

3. Graph Databases
Id Name
1 Bob
2 Tom
3 Joe
Person Knows
1 2
1 3
Person
Crosswalk
Bob
Joe
TomKnows
Knows

4. Tinkerpop
Abstraction layer
Query Language
Computing

5. Graphs with Spark
GraphX
Pregel
GraphLab

6. Why Titan
Flexible backend
No added infrastructure cost
Community support

7. Backends
Cassandra
Hbase
Hazelcastcache
Persistit
Berkeley

8. Text
Database Triangle
http://blog.nahurst.com/visual-guide-to-nosql-systems

9. HBase
Strong consistency at the record level
Transaction support
Store procedures
Replication

10. Cassandra
Tunable consistency
Multiple datacenter support
Built in replication and fault tolerance
CQL query language
Keyspace passwords

11. Indexing
Built-in
Fast for exact matches
Lucene
More advanced queries
Good for single box
Elasticsearch
Advanced queries
large scale clusters

12. Gremlin vs SPARQL
Support for complex queries
http://gremlindocs.com/
Easy query language
http://www.w3.org/TR/rdf-
sparql-query/
Gremlin SPARQL

13. Gremlin vs SPARQL example 1
g.v(‘tg:1')
.out('tg:knows')
SELECT ?x WHERE {
tg:1 tg:knows ?x
}
Gremlin SPARQL

14. g.v(‘tg:1')
.out(‘tg:knows')
.out('tg:name')
SELECT ?y WHERE {
tg:1 tg:knows ?x .
?x tg:name ?y
}
Gremlin SPARQL
Gremlin vs SPARQL example 2

15. Our Mission
▪Deliver the most current information on the U.S. healthcare provider
universe using integrated solutions in order for customers to:
› Prevent fraud, waste and abuse across the healthcare system
› Comply with evolving state and federal regulations
› Improve market opportunity for non retail drugs and devices
Health Market Science a Lexisnexis Company

16. The Business
Business
SolutionsHealth Care Provider & Facilities
Variety/Velocity
• >2000 of sources
• 6 Million unique HCPs
• 10+ years history
Data Challenges
• Constant change in real
world data
• Conflicting & partial info
• Frequent changes to source
structure
• Authoritative sources vs.
crowdsource
• Predicting source quality
Master Data Solutions
Medical Procedures & Diagnosis
Volume/Velocity
• ~1B claims annually
• +5B records annually
• 5+ years history
Data Challenges
• Sources have incomplete
capture
• Overlapping source data
• Statistical projections &
biases
• Social media type
relationships
Medical Claims Data
Batch
(CompleteView,
Expense Manager)
Transactional
(PRS/MDM/
VerifyRx)
Big Data Relational
DB & Analytics
(Claims)

17. Master Data Management
Visualization
Dashboard / Reports
Structured Storage
RelationalIndexing
Flexible Storage
NoSQL Graph(s)
Interfacing
Web Services
Distributed Processing
Standardize
Validate
Match
Consolidate
Analytics
Data Sources
Government
Web
Customer
I’m happy
User Interface

18. Our use of Titan
Link storage
Analytics of links
Affiliation of business influences
Visualization of relationships

19. Demo