How To Fix MDQ_RM_PROCESS012 - Algorithm execution failed for mining run (&1).

SAP Error Message - Details

• Message type: E = Error

• Message class: MDQ_RM_PROCESS - MDQ Rule Mining Process Message Class

• Message number: 012

• Message text: Algorithm execution failed for mining run (&1).

What causes this issue?

An error occurred in the algorithm execution in the HANA Embedded
Machine Learning Interface. Root cause can be the algorithm execution on
the database, the system environment, or configuration.
This needs further analysis.

How to fix this error?

Please contact your system administrator.

Procedure for System Administrators

The system issues an error message and will not allow you to continue with this transaction until the error is resolved.

Error message extract from SAP system. Copyright SAP SE.

Smart SAP Assistant

• What is the cause and solution for SAP error message MDQ_RM_PROCESS012 - Algorithm execution failed for mining run (&1). ?

  The SAP error message MDQ_RM_PROCESS012 indicates that there was a failure during the execution of an algorithm for a mining run in the SAP Business Warehouse (BW) or SAP Predictive Analytics environment. This error can occur for various reasons, and understanding the cause is essential for troubleshooting and resolving the issue.
  Possible Causes:
  
  Data Quality Issues: The data being processed may have inconsistencies, missing values, or outliers that prevent the algorithm from executing properly.
  Configuration Errors: Incorrect settings in the mining model or the mining run configuration can lead to execution failures.
  Resource Limitations: Insufficient memory or processing power on the server can cause the algorithm to fail, especially with large datasets.
  Algorithm-Specific Issues: Certain algorithms may have specific requirements or limitations that, if not met, can lead to execution failures.
  Version Compatibility: If there are discrepancies between the versions of the software components being used, it may lead to unexpected behavior.
  
  Solutions:
  
  
  Check Data Quality:
  
  Review the dataset