Fusion AI 4.1.0 Release Notes

Release date: 17 July 2018

Improvements

  • SQL Engine Improvements

    • Added virtual table join support to the Solr/SQL pushdown strategy (SQL 360 Collections)

    • Added Kerberos authentication and SSO authentication support

    • Fixed scaling bugs with Tableau

    • Added robust Tableau support (better than MySQL and PostGres on cross table joins)

  • Experiment management improvements

    • New "default response time in ms" default metric

  • The Machine Learning index stage and the Machine Learning query stage now support multiple values when specifying field names, in this format: field1:weight,field2:weight,field3:weight

  • Job improvements

    • Custom stopword lists can now be uploaded to the blob store and utilized by the Head/Tail Analysis, Document Clustering (formerly the Bisecting KMeans Clustering job), Cluster Labeling, Phrase Extraction (formerly the Statistically Interesting Phrases job), and Token and Phrase Spell Correction jobs.

    • The Levenshtein Spell Checking job has been removed. Use the Token and Phrase Spell Correction job instead.

    • Add schema groups and categories by default on all Spark Jobs

    • Some jobs have been renamed:

      4.0 Job 4.1 Job

      Co-occurrence Similarity

      Legacy Item Similarity

      Item Similarity Recommender

      Legacy Item Recommender

      Matrix Decomposition Based Query - Query Similarity

      Query to Query Similarity Computation

      Statistically Interesting Phrases

      Phrase Extraction

    • Custom stopword lists can now be uploaded to the blob store and utilized by the Head/Tail Analysis, Document Clustering (formerly the Bisecting KMeans Clustering job), Cluster Labeling, Phrase Extraction (formerly the Statistically Interesting Phrases job), and Token and Phrase Spell Correction jobs.

    • The Levenshtein Spell Checking job has been removed. Use the Token and Phrase Spell Correction job instead.

    • Add schema groups and categories by default on all Spark Jobs

  • Spark/Solr improvements

    • Improved logging

    • For jobs that support it, the fieldToVectorize property now supports multiple values, in this format: field1:weight,field2:weight,field3:weight.

  • The Machine Learning index stage and the Machine Learning query stage now support multiple values when specifying field names, in this format: field1:weight,field2:weight,field3:weight

Other changes

  • When you disable signals for a collection, the associated jobs are now deleted.

  • The Boost with Signals query pipeline stage can now be configured to point to an alternative collection for aggregated signals.