Cerved Datascience Milan
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Cerved is an Italian data and analytics company. It uses graph databases and network analysis to better understand complex relationships within data from over 50 sources. Cerved started using graph databases in 2011 to improve an algorithm, and has expanded usage to better link corporate data and power solutions through visualization and machine learning algorithms on graph data. Graph databases allow flexible and connected modeling of Cerved's data, and native graph storage and processing improves querying, integration, and analysis compared to relational databases.
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Cerved Datascience Milan
- 1. 25 Settembre 2017 With a datascience perspective Aview of graph data usage by Cerved Stefano Gatti – Head of Innovation and data sources Nunzio Pellegrino – Senior Data Scientist – Innovation team
- 2. Cerved and its graphs in a nutshell
- 3. 3 Cerved, in a nutshell The Italian data-driven company CREDIT INFORMATION Protection against credit risk MARKETING SOLUTIONS New business opportunities CREDIT MANAGEMENT Manage and collect performing and non-performing loans. Over 1000 a minute ü Documents Over 40 million ü Lines of code Over 30,000 ü Customers Over 50 different ü Data sources Over 10million a day ü Api call Over 1,900 ü People 377 million Eur (2016) ü Revenue
- 5. 5 Cerved, in a tech view Data Algorithms Solu0ons Towards algorithmic economy …
- 6. 6 Cerved Graph Story 2011-12 - we started from an IT problem: reengineering of beneficial owner algorithm
- 7. 7 Cerved Graph Story 2014-15 - we went through a more algorithmic problem: corporate linkages algorithm
- 8. 8 Cerved Graph Story 2015-16 - we go with a “full stack” solution
- 9. 9 Cerved Graph thoughts We strongly believe in … The power of linking data The power of analyzing data with network analysis The power of visualizing data in a different way To understand a li5le be5er the increasing complexity of modern world … also from an economic point of view
- 10. Why a Graph Database?
- 11. 11 What is a Graph?
- 12. 12 Key Concepts Graph database NoSQL database Managing highly connected data and complex queries Flexible data model
- 13. 13 Key Concepts Graph database Declarative or imperative language Horizontal Scaling Graph native storage and process
- 14. 14 Where graphdb can be useful? “Hands-On Machine Learning with Scikit-Learn and TensorFlow” by Aurélien Géron
- 15. 15 Maybe in the future… “Hands-On Machine Learning with Scikit-Learn and TensorFlow” by Aurélien Géron
- 16. 16 Frame the Problem Data Model Simple1 Expressive2 3 Additive
- 17. 17 RDBMS RDBMS vs Graph Data Model Graph
- 18. 18 Store & Get Data Native graph storage Store Data Fast Write Performance Easy Data Integration: CSV Jdbc REST Api
- 19. 19 Store & Get Data Native graph storage Store Data Fast Write Performance Easy Data Integration: Get Data Native graph processing à Index free adjacency CSV Jdbc REST Api Cypher, Declarative Language Driver: Python py2neo (unofficial) R (unofficial) Java APOC
- 20. 20 Explore Data Transform implicit to explicitCypher (access points, pattern)
- 21. 21 Explore Data Transform implicit to explicitCypher (access points, pattern)
- 22. 22 Explore Data Transform implicit to explicitCypher (access points, pattern)
- 23. 23 Prepare Data Feature Creation with parallel Graph algorithms Centralities • Page Rank • Betweenness Centrality • Closeness Centrality Graph Partitioning • Label Propagation • Connected Components • Strongly Connected Components Path Finding • Minimum Weight Spanning Tree • All Pairs- and Single Source Shortest Path
- 24. 24 Prepare Data Feature Creation with parallel Graph algorithms Centralities • Page Rank • Betweenness Centrality • Closeness Centrality Graph Partitioning • Label Propagation • Connected Components • Strongly Connected Components Path Finding • Minimum Weight Spanning Tree • All Pairs- and Single Source Shortest Path Graph Size (GB) nodes (M) rels (M) PageRank (s) ConCom (s) LabelPropag (s) StrongConCom (s) Pokec 7.3 2 31 10 24 12 12 DBPedia 15 11 117 46 91 51 65 Graphs500-23 7.9 5 129 19 29 18 25 Twitter-2010 49 42 1468 349 353 405 339 soc-LifeJournal1 6.3 5 69 30 34 25 23 Friendster 62 66 1806 611 619 296 483 Performance
- 25. 25 Present&Launch your solution Real time Recommendation Fraud Detection Social Network Analysis Search & Link Analysis Knowledge Graph Natural Language Process
- 26. Nunzio Pellegrino S e n i o r D a t a S c i e n t i s t – Innovation Team nunzio.pellegrino@cerved.com Stefano Gatti Head of Innovation & Data Sources stefano.gatti@cerved.com