When Machine Learning meets Graph Databases

Machine Learning is everywhere these days (just after AI), it started as a python and R thing, it joined the Oracle Database after and it’s now available for Oracle Graph Database as well. Let’s go through some examples of how graphs require to slightly adapt data preparation to run Machine Learning algorithms.

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4. Vertex edge Graph Database (also called node)
5. edge edge label edge properties edge ID directed edge vertex (node) vertex properties vertex ID a vertex can have a label
8. Scalable and Persistent Storage Graph Data Access Layer API Graph Analytics In-memory Analytic Engine Blueprints & SolrCloud / Lucene Property Graph Support on Files, Apache HBase, Oracle NoSQL or Oracle DB 12.2+ REST Web Service Python, Perl, PHP, Ruby, Javascript, … Java APIs Java APIs/JDBC/SQL/PLSQL Cytoscape Plug-in R Integration (OAAgraph) Spark integration SQL*Plus, … PGX
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14. How much? by Francesco Tisiot (34)
15. How much?
16. How much?
17. How much? 1’000 more columns of features
18. How much? (100’000 rows of houses with a price) Training
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22. Customer 1 Customer 3 Customer 2 Product 2 Product 3 Product 4 Product 5 Product 1 Customer 1 is more similar to Customer 3 than Customer 2
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32. • • • • • At least for now…