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)
   

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5. edge
   edge label
   edge properties
   edge ID
   directed edge
   vertex (node)
   vertex
   properties
   vertex ID
   a vertex can
   have a label
   

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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)
    

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15. How much?
    

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16. How much?
    

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17. How much?
    1’000 more columns of features
    

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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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    At least for now…
    

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