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17 NOV 2016 @ BIG DATA SPAIN
@StratioBD
MULTIPLATFORM SOLUTION FOR GRAPH DATASOURCES
Multiplatform Spark solution for Graph datasourcess, Stratio Stratio
Javier Domínguez
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.
LET'S HAVE FUN!
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
INTRODUCTION
@StratioBD
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
VALUE IS THE DATA VALUE IS UNDERSTANDING THE DATA
DO NOT STAY ON THE SURFACE OF KNOWLEDGE
MULTIPLATFORM SOLUTION FOR GRAPH DATASOURCES
• Graph use cases
• DataStores
• Machine learning
@StratioBD
Example of how to exploit a massive database from different stages and through several graph technologies
MACHINE LEARNING LIFE CYCLE WITH BIG DATA
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
USE CASES
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 themval 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'")
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
DATASTORES
SparkApache 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++
GraphFramesIt 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
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
MACHINE LEARNING
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.
DEMO
• Dataset
• Main process explanation
• Notebooks show off
@StratioBD
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
DATASET
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
PROCESS EXPLANATION
PROCESS EXPLANATION
Transforms
CastRDF Dataset
GraphFrames Batch query
Neo4jGraphXExtracts sample & transforms Online
query
SVDK-core
Decomposition Strongly connected graph
Apply algorithms
Behavior Inference
Graph
Subject equality
PROCESS EXPLANATION
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
NOTEBOOKS SHOW OFF
BUSINESS EXAMPLE
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
40BJaccard distance calculation
in everyday process
400Knodes graph clustering
BANK USE CASE
THE END
• Results
• What's next?
@StratioBD
WHAT'S NEXT?
Semantic search engineInclude 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
THANK YOU
UNITED STATESTel: (+1) 408 5998830
EUROPETel: (+34) 91 828 64 73
www.stratio.com
@StratioBD
