One of the top banks in Europe, needed a system to provide better performance, scaling almost linearly with the increase in information to be analyzed, and allowing to move the processes that were currently being executed in the Host to a Big Data infrastructure. During a year we've worked on a system which is able to provide greater agility, flexibility and simplicity for the user to view information when profiling and is now able to analyze the structure of profile data. It's a powerful way to make online queries to a graph database, which is integrated with Apache Spark and different graph libraries. Basically, we get all the necessary information through Cypher queries which are sent to a Neo4j database. Using the last Big Data technologies like Spark Dataframe, HDFS, Stratio Intelligence or Stratio Crossdata, we have developed a solution which is able to obtain critical information for multiple datasources like text files o graph databases.

1. @StratioBD
Multiplatform Spark solution for Graph datasourcess, Stratio Stratio
Javier Domínguez

2. Javier Dominguez Montes
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. Graph use cases Results
What's next?
Dataset
Main process explanation
Notebooks show off
DataStores
Machine learning
Business example

5. @StratioBD

6. 500 GB - 2 TB
4 TB - 8 TB
20 GB - 100 GB
80’S 2000 2010 2015 2020
100 TB
> 10 PB

7. VALUE IS THE DATA
VALUE IS UNDERSTANDING THE DATA

8. DO NOT STAY ON THE
SURFACE OF KNOWLEDGE

9. • Graph use cases
• DataStores
• Machine learning
@StratioBD

10. Example of how to exploit a massive database from different stages and
through several graph technologies
MACHINE LEARNING LIFE CYCLE WITH BIG
DATA

11. 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!

12. USE CASES

13. 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'")

14. 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

15. DATASTORES

16. 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.

17. 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.

18. MACHINE LEARNING

19. 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.

20. • Dataset
• Main process explanation
• Notebooks show off
@StratioBD

21. 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

22. DATASET

23. 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

24. PROCESS
EXPLANATION

25. Transforms
Cast
RDF
Dataset
GraphFrames
Batch
query
Neo4jGraphX
Extracts sample & transforms Online
query

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

27. 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

28. NOTEBOOKS SHOW
OFF

29. BUSINESS EXAMPLE

30. 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

31. • Results
• What's next?
@StratioBD

32. 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

33. THANK YOU
UNITED STATES
Tel: (+1) 408 5998830
EUROPE
Tel: (+34) 91 828 64 73
contact@stratio.com
www.stratio.com
@StratioBD

34. people@stratio.com
WE ARE HIRING
@StratioBD