Determining candidate score and potential

  • Rversion finale: Australia
  • Mis à jour 12 mars 2026
  • 2 minutes de lecture

    • ERP Semantic Mining generates a score to rank the potential for replatforming legacy ERP (Enterprise Resource Planning) candidates onto the ServiceNow AI Platform.

    Important :
    Starting with the Zurich release, ERP Semantic Mining is being prepared for future deprecation. It will be hidden and no longer activated on new instances but will continue to be supported.
    Every candidate has an ERP module specified in the candidate details in ERP Semantic Mining. The ERP module is used to evaluate the potential score of the candidate for replatforming, as well as the remote tables and extraction tables the model contains.
    • Remote tables get their records from running an associated script against an external data source.
    • Extraction tables retrieve large amounts of data using a scheduled query, and use transform tables to process data for use on the ServiceNow AI Platform.
    Remarque :

    Admins must first configure the connection to the ERP system in Zero Copy Connector for ERP. For more information, see Working with ERP systems in Zero Copy Connector for ERP.

    High and low scores for candidate potential

    ERP Semantic Mining evaluates candidates based on how well their tables and fields are supported by the ServiceNow AI Platform.
    • A high potential indicates that ERP Semantic Mining can immediately use remote tables and extraction tables that match the ERP model for the application candidate without making additional changes.
    • A low potential indicates that the application candidate matches few of the remote tables and extraction tables in the ERP models in Zero Copy Connector for ERP.

    How scores are calculated

    The candidate potential score is calculated using the following metrics:
    • ERP models: The number of ERP models that the candidate uses remote tables and extraction tables from.
    • Similar candidates: The number of candidates with a similarity score above the threshold, which accounts for both table-based similarity and model-based similarity.

      The threshold can be adjusted in the System Properties [sys_properties] table, and the default OR condition can be changed to AND.

    • Supported table score: The ratio of the number of custom app tables that are supported by any ERP model relative to the number of custom app tables.
      Remarque :
      Tables from either the Technical or ServiceNow table clusters are ignored from these computations.
    • Supported table usage: The ratio of tables supported by the relevant ERP models that are used by the custom app.
    • Unsupported model penalty: A penalty for the number of unsupported operations on tables in ERP models. The number of unsupported operations is passed through a sigmoid function, so it ranges from 0.0 and 1.0.
    • Unsupported table extensions: The number of custom app tables that are also suggested as model extensions.
    • Model inaccuracy: The number of tables supported by relevant ERP models that aren’t used by custom apps, and are passed through a sigmoid function.