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System Anomaly Prediction - Infrastructure Preparation

First prerequisite is, that the SAP Focused Run Master Guide is completely implemented. System Anomaly Prediction (until SAP Focused Run 4.0 SP00) requires you to additionally install R. Before you proceed with the setup, kindly refer the R sizing SAP Note 2686042.

Preparation for Using R Server as ML Platform

When you prepare the Linux compilation environment for R with the various dependencies, you must install various SDK packages in your Linux environment. These SDK packages are usually not installed on server hosts running SAP software and therefore are not part of your standard repositories for Linux installations with distributions like SuSe SLES for SAP or Red Hat for Enterprise. You might need to register additional Linux software repositories on your designated R host.

We recommend preparing the compilation environment and the R runtime on dedicated hosts, following your company security policies.

Note: The guide is currently valid for SUSE SLES only. 

Please execute the guide completely and verify the installation with chapter 6.4 Validation.

Configure Anomaly Engine and Model Deployment


  1. Log in to your SAP Focused Run ABAP system on the production client.
  2. Start transaction SM30.
  3. Enter as table name: PAS_SM_GEN_CONFI.
  4. Choose Maintain.
  5. Choose New Entries and provide the following values:
    • Param Count: 1
    • Param Value: 30 (Number of days, for which the prediction data is retained)
  6. Choose Save.
  7. Choose New Entries and provide the following values:
    • Param Name: RMODELPATH
    • Param Count: 1
    • Param Value: <Enter the file system path on the R Host, to which the model files are to be copied (see item 12 below)>
  8. Choose Save.

    : RMODELPATH is case sensitive. Alternatively use the report program “MAINTAIN_R_MODEL_PATH” to update the RMODELPATH.

  9. Download the latest model definition from SAP Note 2706779.
  10.  Upload to SAP Focused Run the latest model definition (.zip file), by running the report PAS_SA_IMPORT_MODEL using transaction SA38.
  11. Finally, download the latest R models from SAP Note 2706779.
  12.  Provide these R models to the R host by copying the files to the file system location mentioned for parameter RMODELPATH (see item 7 above).

Roles and Authorization

The configuration can be done in the System Monitoring application only.

  • System Anomaly Prediction Configuration
    • SAP_FRN_AAD_MOAL_ALL - All authorizations for System Monitoring & Alert Management Administration/Configuration
  • System Anomaly Prediction Display
    • SAP_FRN_APP_MOAL_DISP - Display authorizations for System Monitoring & Alert Management
    • SAP_FRN_APP_MOAL_ALL - All authorizations for System Monitoring & Alert Management

System Analysis:

  • System Anomaly Prediction Display
    • SAP_FRN_APP_SYA_ALL - All authorizations for the System Analysis application (end user)

Schedule Job

Schedule the following background jobs, using transaction SM36, and select as Target, the previously-created Job Server Group FRN_JOB_PUBLIC. These jobs cannot be scheduled via task list and must be scheduled manually.

Job NameDefine Step UserABAP Program NameStart TimePeriod Value





5 Min




00:30 am



If you as an administrator have the roles for system anomaly prediction configuration, you can activate and maintain models for prediction scenario. The concept of variant is not available from SAP Focused Run 2.0 onwards. You need to run the migration report PAS_CONF_MIGRATION to migrate from variant-based configuration to system-based configuration. If you do not run this report program, you will not see the systems which were activated for prediction in SAP Focused Run 1.0 SP00 now in the new SAP Focused Run 3.0.

Personal Data

Personal Data Identification and Deletion in Scenario of System Anomaly Prediction

Predictive applications store the user ID in the following tables;


If you want to check whether personal data is stored in the application, you can execute the report PAS_PERS_DATA_USAGE.

Personal data that is stored in the application can be deleted by running the report PAS_PERS_DATA_DELETE.

The execution of the above-mentioned reports is logged in SLG1 using object “PAS”.

Support Component

You can raise your incidents in the support component SV-FRN-APP-SYM.