This document discusses the integration of knowledge and information through graph analysis in the context of Aurelius' graph database Titan. It highlights features like real-time traversal, scalability, and analytics capabilities, along with examples of graph properties involving mythical figures. The document also touches on the use of HDFS for storage and the processing improvements brought by Apache's frameworks.

1. KNOWLEDGE
INFORMATION
DATA
Adding Value Through Graph
Analysis
Matthias Broecheler, CTO
@mbroecheler AURELIUS
March V, MMXIII THINKAURELIUS.COM

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Communities of Interest
Finding Influencers
"
Understanding Behavior
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Information Integration
Recommendation
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Question Answering
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4. " "
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Fraud Detection
Risk Analysis
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Market Valuation
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5. Knowledge
Value
Information
Data

6. likes(Jane Joe, cute mamals):0.8
Knowledge
userid:3552
"
clicked
timestamp:
addid:9914
Information
93932342
"
2013-03-03 18:52:48:112;
12.123.211.192; ACCESS/TRR;
http://adserve.domain.com/
render.cgi?
uid=F32282DA39B&flagtru&xls=trendi Data
ng ; ACTION=CLICK|DELAY=250|x=450|
y=632!

7. Graph Databases
&
likes(Jane Joe, cute mamals):0.8
Graph Analysis
Knowledge
userid:3552
"
clicked
timestamp:
addid:9914
Information
93932342
"
2013-03-03 18:52:48:112;
12.123.211.192; ACCESS/TRR;
http://adserve.domain.com/
render.cgi?
uid=F32282DA39B&flagtru&xls=trendi Data
ng ; ACTION=CLICK|DELAY=250|x=450|
y=632!

11. I
Graph Foundation
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12. name: Neptune
name: Alcmene
type: god
type: god
Vertex
Property
name: Saturn
name: Jupiter
name: Hercules
type: titan
type: god
type: demigod
name: Pluto
name: Cerberus
type: god
type: monster
Graph

13. name: Neptune
name: Alcmene
type: god
type: god
Edge
brother
mother
name: Saturn
name: Jupiter
name: Hercules
type: titan
type: god
type: demigod
father
father
Edge
battled
brother
Property
time:12
name: Pluto
name: Cerberus
type: god
type: monster
Edge
Type pet
Graph

14. name: Neptune
name: Alcmene
type: god
type: god
brother
mother
name: Saturn
name: Jupiter
name: Hercules
type: titan
type: god
type: demigod
father
father
battled
brother
time:12
name: Pluto
name: Cerberus
type: god
type: monster
pet
Path

15. name: Neptune
name: Alcmene
type: god
type: god
brother
mother
name: Saturn
name: Jupiter
name: Hercules
type: titan
type: god
type: demigod
father
father
battled
brother
time:12
name: Pluto
name: Cerberus
type: god
type: monster
pet
Degree

16. Apache 2
Aurelius Graph Cluster
TITAN FAUNUS FULGORA
Map/Reduce
Load
Bulk Load
Analysis results
back into Titan
Stores a massive-scale Batch processing of large Runs global graph algorithms
property graph allowing real- graphs with Hadoop
on large, compressed,
time traversals and updates
in-memory graphs

17. II
Titan Graph Database
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18. Titan Features
 Numerous Concurrent Users
 Many Short Transactions
 read/write
 Real-time Traversals (OLTP)
 High Availability
 Dynamic Scalability
 Variable Consistency Model
 ACID or eventual consistency
 Real-time Big Graph Data

19. Storage Backends
Partitionability
Consistency
Availability

20. $ ./titan-0.2.0/bin/gremlin.sh!
! ! !,,,/!
(o o)!
-----oOOo-(_)-oOOo-----!
gremlin> g = TitanFactory.open('/tmp/titan')!
==>titangraph[local:/tmp/titan]!
gremlin> v = g.V(‘name’,’Hercules’)!
==>v[4]!
gremlin> v.out(‘father’).out(‘brother’).name!

21. name: Neptune
name: Alcmene
type: god
type: god
brother
mother
name: Saturn
name: Jupiter
name: Hercules
type: titan
type: god
type: demigod
father
father
battled
brother
time:12
name: Pluto
name: Cerberus
type: god
type: monster
pet
gremlin> v.out(‘father’).out(‘brother’).name!

22. Vertex-Centric Indices
 Sort and index edges per
vertex by primary key
 Primary key can be composite
 Enables efficient focused
traversals
 Only retrieve edges that matter
 Uses push down predicates for
quick, index-driven retrieval

23. battled
battled
battled
time: 1
time: 3
time: 5
mother
battled
v
v.query()!
time: 9
father
fought
fought

24. battled
battled
battled
time: 1
time: 3
time: 5
mother
battled
v
v.query()!
time: 9
.direction(OUT)!
father

25. battled
battled
battled
time: 1
time: 3
time: 5
battled
v
v.query()!
time: 9
.direction(OUT)!
.labels(‘battled’)!

26. battled
battled
time: 1
time: 3
v
v.query()!
.direction(OUT)!
.labels(‘battled’)!
.has(‘time,T.lt,5)!

27. Titan Features
I. Data Management
II. Vertex-Centric
Indices

28. Titan Features
III. Graph
Partitioning
IV. Edge Compression

29. III
TITAN 0.3.0 [-SNAPSHOT]
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30. Titan Embedding
 Rexster RexPro
 lightweight Gremlin
Server
 binary protocol
 Titan Gremlin Engine
 Embedded Storage
Backend
 in-JVM method calls
 Native clients
 Java, Python, Clojure

31. Graph Indexing
 Vertex and Edge indexing
 Pluggable index provider
 ElasticSearch
 Lucene
 Full-text search
 Numeric range search
 Geographic search

32. name: Neptune
name: Alcmene
age: 5200
age: 3300
title: God of the
earth and ocean
brother
mother
name: Jupiter
name: Saturn
age: 4800
name: Hercules
age: 5900
title: God of the title: Divine hero
heaven and skies
father
father
battled
brother
time:12
location: (38.071,23.745)
name: Pluto
name: Cerberus
age: 4900
title: Ugly beast of the
title: God of the
underworld
underworld
pet

33. name: Neptune
name: Alcmene
age: 5200
age: 3300
title: God of the
earth and ocean
brother
mother
name: Jupiter
name: Saturn
age: 4800
name: Hercules
age: 5900
title: God of the title: Divine hero
heaven and skies
father
father
battled
brother
time:12
location: (38.071,23.745)
name: Pluto
name: Cerberus
age: 4900
title: Ugly beast of the
title: God of the
underworld
underworld
pet
g.query().has(‘age’,Cmp.GREATER_THAN,5000).vertices()!

34. name: Neptune
name: Alcmene
age: 5200
age: 3300
title: God of the
earth and ocean
brother
mother
name: Jupiter
name: Saturn
age: 4800
name: Hercules
age: 5900
title: God of the title: Divine hero
heaven and skies
father
father
battled
brother
time:12
location: (38.071,23.745)
name: Pluto
name: Cerberus
age: 4900
title: Ugly beast of the
title: God of the
underworld
underworld
pet
g.query().has(‘title’,Txt.CONTAINS,’god’).vertices()!

35. name: Neptune
name: Alcmene
age: 5200
age: 3300
title: God of the
earth and ocean
brother
mother
name: Jupiter
name: Saturn
age: 4800
name: Hercules
age: 5900
title: God of the title: Divine hero
heaven and skies
father
father
battled
brother
time:12
location: (38.071,23.745)
name: Pluto
name: Cerberus
age: 4900
title: Ugly beast of the
title: God of the
underworld
underworld
pet
g.query().has(‘age’,Cmp.GREATER_THAN,5000)
has(‘title’,Txt.CONTAINS,’god’).vertices()!

36. name: Neptune
name: Alcmene
age: 5200
age: 3300
title: God of the
earth and ocean
brother
mother
name: Jupiter
name: Saturn
age: 4800
name: Hercules
age: 5900
title: God of the title: Divine hero
heaven and skies
father
father
battled
brother
time:12
location: (38.071,23.745)
name: Pluto
name: Cerberus
age: 4900
title: Ugly beast of the
title: God of the
underworld
underworld
pet
g.query().has(‘location’,Geo.WITHIN,
Geoshape.circle(38,23,100).edges()!

37. IV
Faunus Graph Analytics
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38. Faunus Features
 Hadoop-based Graph
Computing Framework
 Graph Analytics
 Breadth-first Traversals
 Global Graph Computations
 Batch Big Graph Data

39. Faunus Architecture
g._()!

40. Faunus Work Flow
g.V.out .out .count()
hdfs://user/ubuntu/
output/job-0/
output/job-1/ graph*
output/job-2/ { sideeffect*
Compressed HDFS Graphs
 stored in sequence files
 variable length encoding
 prefix compression

41. Apache 2
Aurelius Graph Cluster
TITAN FAUNUS FULGORA
Map/Reduce
Load
Bulk Load
Analysis results
back into Titan
Stores a massive-scale Batch processing of large Runs global graph algorithms
property graph allowing real- graphs with Hadoop
on large, compressed,
time traversals and updates
in-memory graphs

42. What’s New
 Faunus 0.1 released
 Bulk Import / Export for Titan
 loaded graph into Titan
 loading derivations into Titan
 RDF support
 Many optimizations
 vertex compression

43. Faunus Setup
$ bin/gremlin.sh !
,,,/!
(o o)!
-----oOOo-(_)-oOOo-----!
gremlin> g = FaunusFactory.open('bin/titan-hbase.properties')!
==>faunusgraph[titanhbaseinputformat]!
gremlin> g.getProperties()!
==>faunus.graph.input.format=com.thinkaurelius.faunus.formats.titan.hbase.TitanHBaseInputFormat
==>faunus.graph.output.format=org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat!
==>faunus.sideeffect.output.format=org.apache.hadoop.mapreduce.lib.output.TextOutputFormat!
==>faunus.output.location=dbpedia!
==>faunus.output.location.overwrite=true!
gremlin> g._() !
12/11/09 15:17:45 INFO mapreduce.FaunusCompiler: Compiled to 1 MapReduce job(s)!
12/11/09 15:17:45 INFO mapreduce.FaunusCompiler: Executing job 1 out of 1:
MapSequence[com.thinkaurelius.faunus.mapreduce.transform.IdentityMap.Map]!
12/11/09 15:17:50 INFO mapred.JobClient: Running job: job_201211081058_0003!

44. Build a Knowledge Graph
 Based on DBPedia
 Graph version of Wikipedia
 ~290 million edges (~1B triples)
1. Bulk load RDF into Faunus
 6 m1.xlarge
2. Convert to property graph
3. Bulk load into Titan
 3 m1.xlarge with Cassandra
4. OLTP+OLAP
 Total Time: ~ 2 hours

45. Graph OLTP
gremlin> g = TitanFactory.open('bin/cassandra.local') !
==>titangraph[cassandrathrift:10.176.213.110]!
gremlin> g.V('name','Random_walker_algorithm').both.name!
==>Random_walk!
==>Segmentation_(image_processing)!
==>Graph_(mathematics)!
==>Laplacian_matrix!
==>Graph!
==>Laplacian_matrix!
==>Electrical_network!
==>Resistor!
==>Electrical_resistance_and_conductance!
==>Ground_(electricity)!
==>Direct_current!
==>Voltage_source!
==>Precomputation!
==>Category:Computer_vision!
==>Random_Walker_(Computer_Vision)!
==>List_of_algorithms!
==>Segmentation_(image_processing)!
==>Watershed_(image_processing)!
==>Random_walker_(computer_vision)!
==>Random_Walker_(computer_vision)!

46. gremlin> g.V('name','Learning').out.out.out.out[0..10].name !
==>Latium!
==>Roman_Kingdom!
==>Roman_Republic!
==>Roman_Empire!
==>Middle_Ages!
==>Early_modern_Europe!
==>Armenian_Kingdom_of_Cilicia!
==>Lingua_franca!
==>Vatican_City!
==>Vulgar_Latin!
==>Romance_languages!

47. Apache 2
Aurelius Graph Cluster
TITAN FAUNUS FULGORA
Map/Reduce
Load
Bulk Load
Analysis results
aureliusgraphs@googlegroups.com
back into Titan
Stores a massive-scale Batch processing of large Runs global graph algorithms
property graph allowing real- graphs with Hadoop
on large, compressed,
time traversals and updates
in-memory graphs

48. Speed of Traversal/Process
The Graph Landscape
Illustration only, not to scale
Size of Graph

49. TINKERPOP.COM

50. Thanks!
Vadas Gintautas
Marko Rodriguez
@vadasg
@twarko
Stephen Mallette
Daniel LaRocque
@spmallette
AURELIUS
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51. We are Hiring
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