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 for missing values, duplicates, or outliers.
  Clean and preprocess the data to ensure it meets the algorithm's requirements.
  
  
  
  Review Configuration:
  
  Verify the settings of the mining model and the mining run.
  Ensure that all parameters are correctly set and compatible with the chosen algorithm.
  
  
  
  Monitor System Resources:
  
  Check the server's memory and CPU usage during the mining run.
  If resource limitations are identified, consider optimizing the server or running the mining job during off-peak hours.
  
  
  
  Test with Different Algorithms:
  
  If possible, try using a different algorithm to see if the issue persists.
  This can help identify if the problem is specific to a particular algorithm.
  
  
  
  Check Logs for Detailed Errors:
  
  Review the logs generated during the mining run for more detailed error messages.
  Logs can provide insights into what went wrong and help pinpoint the issue.
  
  
  
  Update Software:
  
  Ensure that all components of the SAP system are up to date.
  Check for any patches or updates that may address known issues with the mining algorithms.
  
  
  
  Consult Documentation:
  
  Refer to the SAP documentation for the specific algorithm being used to understand its requirements and limitations.
  
  
  
  Seek SAP Support:
  
  If the issue persists after trying the above solutions, consider reaching out to SAP support for further assistance.
  
  
  
  Related Information:
  
  SAP Notes: Check for any relevant SAP Notes that may address this specific error or provide additional troubleshooting steps.
  Community Forums: Engage with the SAP community forums where other users may have encountered similar issues and can share their solutions.
  Training and Documentation: Familiarize yourself with the SAP Predictive Analytics and BW documentation to better understand the algorithms and their configurations.
  
  By systematically addressing these areas, you should be able to identify the cause of the error and implement an appropriate solution.