GRAPH-BASED DATA INTEGRATION AND ANALYSIS WITH … · 2017-04-06 · Hadoop-based framework for...
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www.scads.de
GRAPH-BASED DATA INTEGRATION AND ANALYSIS WITH GRADOOP
ERHARD RAHM,MARTIN JUNGHANNS, ANDRÉ PETERMANN, ERIC PEUKERT
Project period: 4 years (10/2014 – 09/2018), option for +3 more years after evaluation
Many involved research groups + many associated partners
Focal point for new research activities
Specialists from computer and domain sciences
NATIONAL BIG DATA COMPETENCE CENTER
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Max Planck Institute of Molecular Cell Biology and Genetics
www.scads.de
COLLABORATION WITH APPLICATION SCIENCES
Big Data Life Cycle Management und Workflows
Efficient Big Data Architecture
Data Quality/Data Integration Visual AnalysisKnowledge Extraction
Life‐Sciences
Material Sciences
Digital Humanities
Environmental and Traffic Sciences
Business Data
Service‐Center
3www.scads.de
W.E. NagelE.Rahm
W. Lehner
K.-P. Fähnrich M. Bogdan C. Rother G. Scheuermann
S. Gumhold
P. Stadler G. Heyer
Life Sciences Gene‐Sequence Alignment (Consulting, Measurement, Parallelization), Myers‐Bit‐
Vector with dedicated hardware Parallelization of Gene‐Imputation‐Tasks UFZ‐Mass‐Spectromy‐Data WorkflowsDigital Humanities Text repository services for Digital Humanities (CTS) OCR‐Workflow on HPC (in progress)Environmental/Traffic sciences Deep Learning & structure recognition in spatial planningMaterial and Engineering Sciences Multi‐Scale VisualisationBusiness Data Condition Monitoring of Renewable Energy Power Plants Real‐Estate Data Fusion Deduplication of Person Data
APPLICATION SCIENCES
COMPUTER SCIENCE RESEARCH
Big Data Life Cycle Management und Workflows
Efficient Big Data Architecture
Data Quality/Data Integration Visual AnalysisKnowledge Extraction
Life‐Sciences
Material Sciences
Digital Humanities
Environmental and Traffic Sciences
Business Data
Service‐Center
5www.scads.de
W.E. NagelE.Rahm
W. Lehner
K.-P. Fähnrich M. Bogdan C. Rother G. Scheuermann
S. Gumhold
P. Stadler G. Heyer
Big Data Lifecycle and Workflows KNIME & HPC Integration
Data Quality and Integration Privacy‐Preserving Data Matching Hadoop‐based deduplication (Dedoop) Graph‐based data integration Holistic data integration
Visual Analysis Porting CV‐Algorithms to GPU Improve Visualization of Large Particle Data Muliti‐Scale Visualization for Big Data
Knowledge Extraction Deep Learning for Structure Recognition in Spatial Planning
Big Data Infrastructure Hardening computation infrastructure (Security) Flexible Cluster management Big Data Framework Execution & Monitoring on HPC Time Series Management and Forecasting Geotemporal Data Storage
Graph‐based data analysis (Gradoop)
COMPUTER SCIENCE RESEARCH (METHODS)
,
“GRAPHS ARE EVERYWHERE”
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,
“GRAPHS ARE EVERYWHERE”
Alice
Bob
Eve
Dave
Carol
Mallory
Peggy
Trent
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,
“GRAPHS ARE EVERYWHERE”
Alice
Bob
Eve
Dave
Carol
Mallory
Peggy
Trent
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“GRAPHS ARE HETEROGENEOUS”
Alice
Bob
AC/DC
Dave
Carol
Mallory
Peggy
Metallica
∪ , ∪
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0.2
0.28
0.26
0.33
0.25
0.26
“GRAPHS CAN BE ANALYZED”
Alice
Bob
AC/DC
Dave
Carol
Mallory
Peggy
Metallica
3.62.82
∪ , ∪
11
“GRAPHS CAN BE ANALYZED“
Assuming a social network1. Determine subgraph2. Find communities3. Filter communities4. Find common subgraph
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all V challenges (volume, variety, velocity, veracity) ease-of-use high cost-effectiveness powerful but easy to use graph data model support for heterogeneous, schema-flexible vertices and edges support for collections of graphs (not only 1 graph) powerful graph operators
graph-based integration of many data sources
versioning and evolution (dynamic /temporal graphs)
interactive, declarative graph queries
scalable graph mining
comprehensive visualization support
GRAPH DATA ANALYTICS: REQUIREMENTS
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COMPARISON
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Graph Database SystemsNeo4j, OrientDB
data model rich graph models(PGM)
focus queries
query language yes
graph analytics no
scalability vertical
Workflows no
persistency yes
dynamic graphs / versioning
no
data integration no
visualization (yes)
COMPARISON (2)
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Graph Database SystemsNeo4j, OrientDB
Graph Processing Systems(Pregel, Giraph)
data model rich graph models(PGM)
genericgraph models
focus queries analytic
query language yes no
graph analytics no yes
scalability vertical horizontal
Workflows no no
persistency yes no
dynamic graphs / versioning
no no
data integration no no
visualization (yes) no
COMPARISON (3)
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Graph Database SystemsNeo4j, OrientDB
Graph Processing Systems(Pregel, Giraph)
Distributed DataflowSystems (Flink Gelly, Spark GraphX)
data model rich graph models(PGM)
genericgraph models
genericgraph models
focus queries analytic analytic
query language yes no no
graph analytics no yes yes
scalability vertical horizontal horizontal
Workflows no no yes
persistency yes no no
dynamic graphs / versioning
no no no
data integration no no no
visualization (yes) no no
An end-to-end framework and research platform forefficient, distributed and domain independent graph
data management and analytics.
WHAT‘S MISSING?
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Data Volume and Problem Complexity
Ease
-of-u
se
Graph Processing Systems
Graph Databases
Graph Dataflow Systems Gelly
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Intro Graph Analytics Graph data Requirements Graph database vs graph processing systems
Gradoop architecture and data integration
Extended Property Graph Model (EPGM) Data organization and operators Implementation
Performance Evaluation
Summary/Outlook
AGENDA
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Hadoop-based framework for graph data management and analysis persistent graph storage in scalable distributed store (Hbase) utilization of powerful dataflow system (Apache Flink) for parallel,
in-memory processing
Extended property graph data model (EPGM) operators on graphs and sets of (sub) graphs support for semantic graph queries and mining
Declarative specification of graph analysis workflows Graph Analytical Language - GrALa
End-to-end functionality graph-based data integration, data analysis and visualization
Open-source implementation: www.gradoop.org
GRADOOP CHARACTERISTICS
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integrate data from one or more sources into a dedicated graph storewith common graph data model
definition of analytical workflows from operator algebra
result representation in meaningful way
END-TO-END GRAPH ANALYTICS
Data Integration Graph Analytics Visualization
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HIGH LEVEL ARCHITECTURE
HDFS/YARNCluster
HBase Distributed Graph Store
Extended Property Graph Model
Flink Operator Implementations
Data Integration
Flink Operator Execution
Workflow Declaration
Visual
GrALa DSLRepresentation
Data flow
Control flow
Graph Analytics Representation
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BIIIG: Business Intelligence on Integrated Instance Graphs
Heterogeneous data sources are integrated within an instance graph by preserving original relationships between data objects transactional and master data
Largely automated extraction of metadata and instance data andtransformation into graphs fusion of matching entities and relations
Extraction of subgraphs (business transaction graphs) related to interrelated business activities
Analysis of graphs/subgraphs with aggregation queries, pattern mining etc.
GRAPH-BASED BUSINESS INTELLIGENCE
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DATA INTEGRATION AND ANALYSIS WORKFLOW
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„Business Intelligence on Integrated Instance Graphs (BIIIG)“ (PVLDB 2014)
Business Transaction Graphs
(3) SubgraphIsolation
(2) Graph integration
Integrated Instance Graph
Domain expert
metadata
(1) Graph transformation
Dat
a So
urce
s
SAMPLE INSTANCE GRAPH
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SCREENSHOT OF NEO4J IMPLEMENTATION
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Intro Graph Analytics Graph data Requirements Graph database vs graph processing systems
Gradoop architecture and data integration
Extended Property Graph Model (EPGM) Data organization and operators Implementation
Performance Evaluation
Summary/Outlook
AGENDA
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includes PGM as special case
support for collections of logical graphs / subgraphs can be defined explicitly can be result of graph algorithms / operators
support for graph properties
powerful operators on both graphs and graph collections
Graph Analytical Language – GrALa domain-specific language (DSL) for EPGM flexible use of operators with application-specific UDFs plugin concept for graph mining algorithms
EXTENDED PROPERTY GRAPH MODEL (EPGM)
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• Vertices and directed Edges
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• Vertices and directed Edges• Logical Graphs
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• Vertices and directed Edges• Logical Graphs• Identifiers
1 3
4
5
21 2
3
4
5
1
2
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• Vertices and directed Edges• Logical Graphs• Identifiers• Type Labels
1 3
4
5
21 2
3
4
5Person Band
Person
Person
Band
likes likes
likes
knows
likes
1|Community
2|Community
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• Vertices and directed Edges• Logical Graphs• Identifiers• Type Labels• Properties
1 3
4
5
21 2
3
4
5Personname : Aliceborn : 1984
Bandname : Metallicafounded : 1981
Personname : Bob
Personname : Eve
Bandname : AC/DCfounded : 1973
likessince : 2014
likessince : 2013
likessince : 2015
knows
likessince : 2014
1|Community|interest:Heavy Metal
2|Community|interest:Hard Rock
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Operators
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Operators
Unary BinaryAlgorithms
* auxiliary
Graph
Collection
LogicalG
raph
Aggregation
Pattern Matching
Transformation
Grouping Equality
Call *
Combination
Overlap
Exclusion
Equality
Union
IntersectionDifference
Gelly Library
BTG Extraction
Frequent Subgraphs
Limit
Selection
DistinctSort
Apply *Reduce *Call *
Adaptive Partitioning
Subgraph
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Combination
Overlap
Exclusion
LogicalGraph graph3 = graph1.combine(graph2);LogicalGraph graph4 = graph1.overlap(graph2);LogicalGraph graph5 = graph1.exclude(graph2);
BASIC BINARY OPERATORS
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3
1 2
1 34
52
12 4
5
3
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udf = (graph => graph[‘vertexCount’] = graph.vertices.size())graph3 = graph3.aggregate(udf)
AGGREGATION
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3
1 34
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3 | vertexCount: 5
UDF
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LogicalGraph graph4 = graph3.subgraph((vertex => vertex[:label] == ‘green’))LogicalGraph graph5 = graph3.subgraph((edge => edge[:label] == ‘blue’))LogicalGraph graph6 = graph3.subgraph(
(vertex => vertex[:label] == ‘green’),(edge => edge[:label] == ‘orange’))
SUBGRAPH
3
1 34
52
3
4
1 2
5
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2UDF
UDF
UDF 3
6
1 2
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GraphCollection collection = graph3.match(“(:Green)‐[:orange]‐>(:Orange)”);
PATTERN MATCHING
3
1 34
52 Pattern
4 5
1 34
2
Graph Collection
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LogicalGraph grouped = graph3.groupBy([:label], // vertex keys[:label]) // edge keys
LogicalGraph grouped = graph3.groupBy([:label], [COUNT()], [:label], [MAX(‘a’)])
GROUPING
Keys
3
1 34
52
+Aggregate
3
a:23 a:84
a:42
a:12
1 34
52
a:13
a:21
4
count:2 count:3
max(a):42
max(a):84
max(a):13 max(a):21
6 7
4
6 7
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SAMPLE GRAPH
[0] Tagname : Databases
[1] Tagname : Graphs
[2] Tagname : Hadoop
[3] Forumtitle : Graph Databases
[4] Forumtitle : Graph Processing
[5] Personname : Alicegender : fcity : Leipzigage : 23
[6] Personname : Bobgender : mcity : Leipzigage : 30
[7] Personname : Carolgender : fcity : Dresdenage : 30
[8] Personname : Davegender : mcity : Dresdenage : 42
[9] Personname : Evegender : fcity : Dresdenage : 35speaks : en
[10] Personname : Frankgender : mcity : Berlinage : 23IP: 169.32.1.3
0
1
2
3
4
5
6 7 8 9
10
11 12 13 14
15
16
17
18 19 20 21
22
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knowssince : 2014
knowssince : 2014
knowssince : 2013
hasInterest
hasInterest hasInterest
hasInterest
hasModeratorhasModeratorhasMember hasMember
hasMember hasMember
hasTag hasTaghasTag hasTag
knowssince : 2013
knowssince : 2014
knowssince : 2014
knowssince : 2015
knowssince : 2015
knowssince : 2015
knowssince : 2013
GROUPING: TYPE LEVEL (SCHEMA GRAPH)
vertexGrKeys = [:label]edgeGrKeys = [:label]sumGraph = databaseGraph.groupBy(vertexGrKeys, [COUNT()], edgeGrKeys, [COUNT()])
[11] Person
count : 6
[12] Forum
count : 2
[13] Tag
count : 3
hasMembercount : 4
knowscount : 10
hasInterestcount : 4
hasTagcount : 4
hasModeratorcount : 2
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26
28
27
25
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personGraph = databaseGraph.subgraph((vertex => vertex[:label] == ‘Person’),(edge => edge[:label] == ‘knows’))
vertexGrKeys = [:label, “city”]edgeGrKeys = [:label]sumGraph = personGraph.groupBy(vertexGrKeys, [COUNT()], edgeGrKeys, [COUNT()])
GROUPING: PROPERTY-SPECIFIC
1 3
[11] Person
city : Leipzigcount : 2
[12] Person
city : Dresdencount : 3
[13] Person
city : Berlincount : 1
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25
26
27
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knowscount : 3
knowscount : 1 knows
count : 2
knowscount : 2
knowscount : 2
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personGraph = databaseGraph.subgraph((vertex => vertex[:label] == ‘Person’),(edge => edge[:label] == ‘knows’))
vertexGrKeys = [:label, “city”]edgeGrKeys = [:label]sumGraph = personGraph.groupBy(vertexGrKeys, [COUNT()], edgeGrKeys, [COUNT()])
GROUPING: PROPERTY-SPECIFIC
1 3
[11] Person
city : Leipzigcount : 2
[12] Person
city : Dresdencount : 3
[13] Person
city : Berlincount : 1
24
25
26
27
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knowscount : 3
knowscount : 1 knows
count : 2
knowscount : 2
knowscount : 2
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GraphCollection filtered = collection.select((graph => graph[‘vertexCount’] > 4));
SELECTION
UDF
vertexCount > 4
1 | vertexCount: 5
2 | vertexCount: 4
0 23
41
5 7 86
1 | vertexCount: 5
0 23
41
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GraphCollection frequentPatterns = collection.callForCollection(new TransactionalFSM(0.5))
CALL (E.G. FREQUENT SUBGRAPHS)
FSM
Threshold: 50%
1
0 1 23
4
5 6 78
9
1013
14
2
3
11 12
15 16
17 18
19 20
4
5
6
21 2322
25 2624
7
8
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Implementation
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GRAPH REPRESENTATION
Id Label Properties Graphs
Id Label Properties SourceId TargetId Graphs
EPGMGraphHead
EPGMVertex
EPGMEdge
Id Label Properties POJO
POJO
POJO
DataSet<EPGMGraphHead>
DataSet<EPGMVertex>
DataSet<EPGMEdge>
Id Label Properties Graphs
EPGMVertex
GradoopId := UUID128‐bit
String PropertyList := List<Property>Property := (String, PropertyValue)PropertyValue := byte[]
GradoopIdSet := Set<GradoopId>
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Id Label Properties
1 Community {interest:Heavy Metal}
2 Community {interest:Hard Rock}
Id Label Properties Graphs
1 Person {name:Alice, born:1984} {1}
2 Band {name:Metallica,founded:1981} {1}
3 Person {name:Bob} {1,2}
4 Band {name:AC/DC,founded:1973} {2}
5 Person {name:Eve} {2}
Id Label Source Target Properties Graphs
1 likes 1 2 {since:2014} {1}
2 likes 3 2 {since:2013} {1}
3 likes 3 4 {since:2015} {2}
4 knows 3 5 {} {2}
5 likes 5 4 {since:2014} {2}
likessince : 2014
likessince : 20131 3
4
5
2
1|Community|interest:Heavy Metal
2|Community|interest:Hard Rock
Personname : Aliceborn : 1984
Bandname : Metallicafounded : 1981
Personname : Bob
Personname : Eve
Bandname : AC/DCfounded : 1973likes
since : 2015
knows
likessince : 20141 2
3
4
5
DataSet<EPGMGraphHead>
DataSet<EPGMVertex> DataSet<EPGMEdge>
GRAPH REPRESENTATION: EXAMPLE
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// input: firstGraph (G[1]), secondGraph (G[2])
1: DataSet<GradoopId> graphId = secondGraph.getGraphHead()2: .map(new Id<G>());3: 4: DataSet<V> newVertices = firstGraph.getVertices()5: .filter(new NotInGraphBroadCast<V>())6: .withBroadcastSet(graphId, GRAPH_ID);7:8: DataSet<E> newEdges = firstGraph.getEdges()9: .filter(new NotInGraphBroadCast<E>())
10: .withBroadcastSet(graphId, GRAPH_ID)11: .join(newVertices)12: .where(new SourceId<E>().equalTo(new Id<V>())13: .with(new LeftSide<E, V>())14: .join(newVertices)15: .where(new TargetId<E>().equalTo(new Id<V>())16: .with(new LeftSide<E, V>());
Exclusion
OPERATOR IMPLEMENTATION
likessince : 2013
likessince : 20141 3
4
5
2
1|Community|interest:Heavy Metal
2|Community|interest:Hard Rock
Personname : Aliceborn : 1984
Bandname : Metallicafounded : 1981
Personname : Bob
Personname : Eve
Bandname : AC/DCfounded : 1973likes
since : 2015
knows
likessince : 20141 2
3
4
5
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IMPLEMENTATION OF GRAPH GROUPING ( PROC. BTW2017)
GroupBy(1,2,3) +GC + GR* + MapAssign edges to groupsCompute aggregatesBuild super edges
Filter + MapExtract super vertex tuplesBuild super vertices
GroupBy(1) + GroupReduce*Assign vertices to groupsCompute aggregatesCreate super vertex tuplesForward updated group members
V
E
MapExtractattributes
Filter + Map Extract group membersReduce memory footprint
Join*Replace Source/TargetIdwith corresponding super vertex id
MapExtractattributes
*requires worker communication
V1 V2
V3
V‘
E1 E2 E‘
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ITERATIVE COMPUTATION OF FREQUENT SUBGRAPHS
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n‐edge3‐edge2‐edge1‐edge
result
collecting intermediate iteration results
searchspace
1‐edgeR C F
2‐edgeG R C F
3‐edgeG R C F
n‐edgeG R C F
G : grow frequent patternsR : report supported patternsC : count global frequencyF : filter by min frequency
Intro Graph Analytics Graph data Requirements Graph database vs graph processing systems
Gradoop architecture and data integration
Extended Property Graph Model (EPGM) Data organization and operators Implementation
Performance Evaluation
Summary/Outlook
AGENDA
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TEST WORKFLOW: SUMMARIZED COMMUNITIES
http://ldbcouncil.org/
1. Extract subgraph containing only Persons and knows relations
2. Transform Persons to necessary information
3. Find communities using Label Propagation
4. Aggregate vertex count for each community
5. Select communities with more than 50K users
6. Combine large communities to a single graph
7. Group graph by Persons location and gender
8. Aggregate vertex and edge count of grouped graph
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TEST WORKFLOW: SUMMARIZED COMMUNITIES
https://git.io/vgozj
1. Extract subgraph containing only Persons
and knows relations
2. Transform Persons to necessary information
3. Find communities using Label Propagation
4. Aggregate vertex count for each community
5. Select communities with more than 50K users
6. Combine large communities to a single graph
7. Group graph by Persons location and gender
8. Aggregate vertex and edge count of grouped graph
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BENCHMARK RESULTS
Dataset # Vertices # Edges
Graphalytics.1 61,613 2,026,082
Graphalytics.10 260,613 16,600,778
Graphalytics.100 1,695,613 147,437,275
Graphalytics.1000 12,775,613 1,363,747,260
Graphalytics.10000 90,025,613 10,872,109,028
16x Intel(R) Xeon(R) 2.50GHz (6 Cores) 16x 48 GB RAM 1 Gigabit Ethernet Hadoop 2.6.0 Flink 1.0-SNAPSHOT
0
200
400
600
800
1000
1200
1 2 4 8 16
Runtim
e [s]
Number of workers
RuntimeGraphalytics.100
1
2
4
8
16
1 2 4 8 16
Speedu
p
Number of workers
SpeedupGraphalytics.100 Linear
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BENCHMARK RESULTS 2
Dataset # Vertices # Edges
Graphalytics.1 61,613 2,026,082
Graphalytics.10 260,613 16,600,778
Graphalytics.100 1,695,613 147,437,275
Graphalytics.1000 12,775,613 1,363,747,260
Graphalytics.10000 90,025,613 10,872,109,028
1
10
100
1000
10000
Runtim
e [s]
Datasets
16x Intel(R) Xeon(R) 2.50GHz (6 Cores) 16x 48 GB RAM 1 Gigabit Ethernet Hadoop 2.6.0 Flink 1.0-SNAPSHOT
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EVALUATION OF GROUPING: SCALABILITY
Speedup for grouping on type Runtime for grouping on type
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Intro Graph Analytics Graph data Requirements Graph database vs graph processing systems
Gradoop architecture and data integration
Extended Property Graph Model (EPGM) Data organization and operators Implementation
Performance Evaluation
Summary/Outlook
AGENDA
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Big Graph Analytics Hadoop-based graph processing frameworks based on generic graphs Spark/Flink: batch/streaming-oriented workflows (rather than interactive OLAP) graph collections not generally supported generally missing: graph-based data integration, built-in support for dynamic graph data
GraDoop (www.gradoop.org) open-source infrastructure for entire processing pipeline: graph acquisition, storage,
integration, transformation, analysis (queries + graph mining), visualization extended property graph model (EPGM) with powerful operators (e.g., grouping, pattern
matching) and support for graph collections leverages Hadoop ecosystem Apache HBase for permanent graph storage Apache Flink to implement operators
ongoing implementation
SUMMARY
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COMPARISON
61
Graph Database SystemsNeo4j, OrientDB
Graph Processing Systems(Pregel, Giraph)
DistributedDataflow Systems (Flink Gelly, Spark GraphX)
data model rich graphmodels (PGM)
genericgraph models
genericgraph models
Extended PGM
focus queries analytic analytic analytic
query language yes no no no
graph analytics no yes yes yes
scalability vertical horizontal horizontal horizontal
Workflows no no yes yes
persistency yes no no yes
dynamic graphs / versioning
no no no no
data integration no no no (yes)
visualization (yes) no no limited
Graph-based data integration unified approach for knowledge graphs and regular data graphs holistic data integration for many sources
Graph analytics automatic optimization of analysis workflows optimized graph partitioning approaches visualization of graphs and analysis results interactive graph analytics dynamic graph data
OUTLOOK / CHALLENGES
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M. Junghanns, A. Petermann, K. Gomez, E. Rahm: GRADOOP - Scalable Graph Data Management and Analytics with Hadoop. Tech. report (Arxiv), Univ. of Leipzig, 2015
M. Junghanns, A. Petermann, N. Teichmann, K. Gomez, E. Rahm: Analyzing Extended Property Graphs with Apache Flink. Proc. ACM SIGMOD workshop on Network Data Analytics (NDA), 2016
M. Junghanns, A. Petermann, M. Neumann, E. Rahm: Management and Analysis of Big Graph Data: Current Systems and Open Challenges. In: Big Data Handbook (eds.: S. Sakr, A. Zomaya) , Springer, 2017 (to appear)
M. Junghanns, A. Petermann, E. Rahm: Distributed Grouping of Property Graphs with GRADOOP. In: Proc. BTW, March 2017 (to appear)
A. Petermann, M. Junghanns, S. Kemper, K. Gomez, N.Teichmann, E. Rahm: Graph Mining for Complex Data Analytics. Proc. ICDM 2016 (Demo paper)
A. Petermann, M. Junghanns, R. Müller, E. Rahm: BIIIG : Enabling Business Intelligence with Integrated Instance Graphs. Proc. 5th Int. Workshop on Graph Data Management (GDM 2014)
A. Petermann, M. Junghanns, R. Müller, E. Rahm: Graph-based Data Integration and Business Intelligence with BIIIG. Proc. VLDB Conf., 2014
A. Petermann, M. Junghanns, R. Müller, E. Rahm: FoodBroker - Generating Synthetic Datasets for Graph-BasedBusiness Analytics. Proc. 5th Int. Workshop on Big Data Benchmarking (WBDB), 2014
A. Petermann; M. Junghanns: Scalable Business Intelligence with Graph Collections. it - Information Technology Special Issue: Big Data Analytics, 2016
REFERENCES
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