This document summarizes a presentation given by Javier Dominguez at Big Data Spain about Stratio's multiplatform solution for graph data sources. It discusses graph use cases, different data stores like Spark, GraphX, GraphFrames and Neo4j. It demonstrates the machine learning life cycle using a massive dataset from Freebase, running queries and algorithms. It shows notebooks and a business example of clustering bank data using Jaccard distance and connected components. The presentation concludes with future directions like a semantic search engine and applying more machine learning algorithms.

2. 17 NOV 2016 @ BIG DATA SPAIN
@StratioBD
MULTIPLATFORM
SOLUTION FOR GRAPH
DATASOURCES
Multiplatform Spark solution for Graph datasourcess, Stratio Stratio
Javier Domínguez

3. Javier Dominguez Montes
CTO SKILLS
PROFILE
JAVIER DOMÍNGUEZ
Studied computer engineering at the
ULPGC. He is passionate about Scala,
Python and all Big Data technologies
and is currently part of the Data
Science team at Stratio Big Data,
working with ML algorithms, profiling
analysis based around Spark.

4. LET'S HAVE FUN!

5. INDEX
1
2
3
4
INTRODUCTION
MULTIPLATFORM SOLUTION FOR GRAPH
DATASOURCES
DEMO
THE END
Graph use cases Results
What's next?
Dataset
Main process explanation
Notebooks show off
DataStores
Machine learning
Business example

6. INTRODUCTION
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7. 500 GB - 2 TB
4 TB - 8 TB
20 GB - 100 GB
80’S 2000 2010 2015 2020
CUSTOMER DATA WILL GROW OVER 100X
100 TB
> 10 PB

8. VALUE IS THE DATA
VALUE IS UNDERSTANDING THE DATA

9. DO NOT STAY ON THE
SURFACE OF KNOWLEDGE

10. MULTIPLATFORM SOLUTION
FOR GRAPH DATASOURCES
• Graph use cases
• DataStores
• Machine learning
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11. Example of how to exploit a massive database from different stages and
through several graph technologies
MACHINE LEARNING LIFE CYCLE WITH BIG
DATA

12. Machine Learning life cycle
Show how a data sciencist is able to take advantage of a Graph
Database through different datasources and technologies thanks to
our solution.
Use as a example a masive dataset.
Query the datasource from different technologies like:
• GraphX
• GraphFrames
• Neo4j
And finally apply Machine Learning over our information!
BIG DATA SPAIN USE CASE

13. USE CASES

14. USE CASES
Making use of a masive graph datasource implies make batch queries over it.
We will need to maken them with our distributed technologies... The easier the better
Batch Queries
Motifs filter example
import org.graphframes._
val g: GraphFrame = Graph(usersRdd,relationshipsRdd0)
// Search for pairs of vertices with edges in both directions between them
val motifs: Dataframe = g.find("(person_1)-[relation]->(person_2); (person_2)-[abilities]->(technology)")
motifs.show()
// More complex queries can be expressed by applying filters.
motifs.filter("person_1.name = 'Javier' AND technology.name = 'Neo4j'")

15. Most of our clients or teammates will need to have fast and easy access to the information.
We would need a way to make easy queries and of course a graphic representation of our data!
We would need of course microservices like REST operations over our datastore.
Online queries
USE CASES

16. DATASTORES

17. Spark
Apache Spark is a fast and generic engine for large-scale data processing.
GraphX
Spark API for the management and distributed calculation of graphs. It comes with a great variety of graph
algorithms:
 Connected componentes
 PageRank
 Triangle count
 SVD++
GraphFrames
It aims to provide both the functionality of GraphX and extended functionality taking advantage of
Spark DataFrames. This extended functionality includes motif finding and highly expressive graph
queries.
DATASTORES

18. Neo4j
Neo4j is a highly scalable native graph database that leverages data relationships as first-class entities.
Big data alone used to be enough, but enterprise leaders need more than just volumes of information to
make bottom-line decisions. You need real-time insights into how data is related.
DATASTORES

19. MACHINE LEARNING

20. MACHINE LEARNING
It's possible to quickly and automatically produce models that can analyze bigger, more complex data
and deliver faster, more accurate results – even on a very large scale. The result? High-value predictions
that can guide better decisions and smart actions in real time without human intervention.
Machine learning
SVD
Will relate all the existing object in our dataset and infer possible
new behaviors.

21. DEMO
• Dataset
• Main process explanation
• Notebooks show off
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22. STRATIO INTELLIGENCE
Integration of different Open Source libraries of distributed machine learning algorithms.
Development environment adapted to each data scientist.
Real-time decision based on models based on machine learning algorithms
Integrated with all components of the Stratio Big Data Platform
Comprehensive knowledge lifecycle management

23. DATASET

24. Freebase aimed to create a global resource that allowed people
(and machines) to access common information more effectively.
This model is based on the idea of converting the declarations of the resources in
expressions with the subject-predicate-object which are called triplets.
Subject: It's the resource, what we are describing.
Predicate: Could be a property or a relationship with the object value.
Object value: Propertie's value or the related subject.
<'Cristiano Ronaldo'> <'Scores in 2014/2015'> 61 .
<'Cristiano Ronaldo'> <'Born in'> 'Portugal' .
Freebase Google
Total triplets: 1.9 Billion
DATASET

25. PROCESS
EXPLANATION

26. PROCESS EXPLANATION
Transforms
Cast
RDF
Dataset
GraphFrames
Batch
query
Neo4jGraphX
Extracts sample & transforms Online
query

27. SVD
K-core
Decomposition Strongly
connected graph
Apply
algorithms
Behavior
Inference
Graph
Subject
equality
PROCESS EXPLANATION

28. A k-core of a graph G is a maximal connected subgraph of G in which all vertices have degree at least k.
Equivalently, it is one of the connected components of the subgraph of G formed by repeatedly deleting all
vertices of degree less than k.
Objective
Remove all nodes with fewer connections.
At the end, we want only the most representative and connected elements in our grah.
In our use case we used K = 5.
K-Core process
PROCESS EXPLANATION

29. NOTEBOOKS SHOW
OFF

30. BUSINESS EXAMPLE

31. Jaccard Graph Clustering
Node Clusterization based on concrete relations optimized for Big
Data environments.
We've developed an straightforward functionality which is able to
detect patterns and clusterize data in a graph database thanks to
daily machine learning processes.
Neo4j
Scala Graph
functionalities
Jaccard
Indexation
Connected
Componentes
Java
HDFS / Parquet
Spark / GraphX
40B
Jaccard distance calculation
in everyday process
400K
nodes graph clustering
BANK USE CASE

32. THE END
• Results
• What's next?
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33. WHAT'S NEXT?
Semantic search engine
Include ElasticSearch for making text searchs as a search engine.
Apply more Machine Learning algorithms
• Connected components: As we've already done, try to cluster information thanks to their relationships.
• PageRank: Measure the importance of a subject.
• Triangle counting: Check posible triangle relationships inside our dataset to avoid redundancy.
New Graph use cases
• Fraud detection
• Recommendation System
• Profiling

34. THANK YOU
UNITED STATES
Tel: (+1) 408 5998830
EUROPE
Tel: (+34) 91 828 64 73
contact@stratio.com
www.stratio.com
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35. people@stratio.com
WE ARE HIRING
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