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Configuring RAD through the configuration XML file

Important

From DataMiner 10.5.9/10.6.0 onwards, RAD configuration settings are no longer stored in the RelationalAnomalyDetection.xml file, and all configuration must be done using either the RAD Manager or the RAD API. The procedure below only applies to versions prior to DataMiner 10.5.9/10.6.0.

In Feature Release versions up to DataMiner 10.5.8, the parameter groups that are monitored by relational anomaly detection are specified in the configuration file C:\Skyline DataMiner\Analytics\RelationalAnomalyDetection.xml. Each DMA has a separate configuration file specifying the groups that are monitored by that DMA. In the file on a given DMA, you can only specify parameters hosted on that DMA.

To add a new parameter group, follow these steps.

  1. On the DMA where you want to configure the parameter groups, open the file C:\Skyline DataMiner\Analytics\RelationalAnomalyDetection.xml.

  2. Configure the file as follows:

    <?xml version="1.0" ?>
<RelationalAnomalyDetection>
  <Group name="[GROUP_NAME]" updateModel="[true/false]" anomalyScore="[THRESHOLD]" minimumAnomalyDuration="[THRESHOLD2]">
    <Instance>[INSTANCE1]</Instance>
    <Instance>[INSTANCE2]</Instance>
    [... one <Instance> tag per parameter in the group]
  </Group>
  [... one <Group> tag per group of parameters that should be monitored by RAD]
</RelationalAnomalyDetection>

    The attributes name and updateModel are required, while anomalyScore and minimumAnomalyDuration are optional. For more information on the available options, see Options for relational anomaly groups.

    In each Instance element, you can specify either a single-value parameter or a table parameter using one of the following formats:

    • Single-value parameter: [DataMinerID]/[ElementID]/[ParameterID]
    • Table parameter: [DataMinerID]/[ElementID]/[ParameterID]/[PrimaryKey]

  3. To make sure the changes take effect, in DataMiner Cube, go to System Center > System settings > analytics config, and disable and re-enable Relational anomaly detection.

Note

In some cases, it can be useful to retrain the internal model used by RAD. This allows you to indicate the periods during which a parameter group was behaving as expected, so that RAD can better identify when the parameters deviate from that expected behavior in the future. To do so, either use the RAD Manager (see Specifying the training range) or the SLNetClientTest tool (see Retraining the internal model used by RAD).

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