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.

Bf71450537acca19e045ae6f7febdf9a?s=128

Gianni Ceresa

May 22, 2019
Tweet

Transcript

  1. None
  2. None
  3. None
  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
  6. None
  7. None
  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
  9. None
  10. None
  11. None
  12. • • • • • • • • • •

    • • • • • • • • • • • • • •
  13. • • • •

  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

  19. None
  20. None
  21. • • •

  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
  23. • • •

  24. None
  25. • • • • • •

  26. None
  27. • • • •

  28. None
  29. • •

  30. • •

  31. • • • • •

  32. • • • • • At least for now…