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When Machine Learning meets Graph Databases

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.

Gianni Ceresa

May 22, 2019
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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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  32. At least for now…

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