SN System Properties Reference
Property reference

sn_disco_workspace.discovery_schedule_anomaly_detection_method

The property defines the method for detecting anomalies in Discovery schedules, allowing either a machine learning or statistics-based approach. Admins should review this setting to optimize anomaly detection based on their organization's needs and data characteristics.

Default
ML-based
Type
choice list
Application
Discovery Admin Workspace

Key questions about this property

The answers below summarize the purpose, scope, default effect, and review scenarios for this property.

What does it do? It specifies whether to use ML-based or Stats-based methods for identifying anomalies in Discovery schedules, impacting the accuracy of detection.
What area does it affect? Discovery Admin Workspace
What does the default mean? The default value is "ML-based", which is the baseline setting used unless it is changed.
When should you review it? Review this property when adjusting anomaly detection strategies or assessing the effectiveness of current methods.

Out of the box property record

Raw metadata from the property record.

Property name sn_disco_workspace.discovery_schedule_anomaly_detection_method
Sys ID 21aef1b8ff1462103612ffffffffff9b
Type choice list
Application Discovery Admin Workspace
Default value ML-based
Description Property to determine the underlying approach used for anomaly detection in Discovery schedules. ServiceNow can use ML-based or Statistics-based (Stats-based) methods to identify anomalous Discovery schedules. The ML-based approach uses historical scan data and AI algorithms to detect anomalies and outliers. The Stats-based approach uses historical scan data and traditional statistical standard deviation metrics to detect anomalies and outliers. The default value is ML-based.
Updated 2025-04-02 18:35:55

Sources

Official references and nearby text excerpts where this property appears.

Documentation excerpt View supporting context
Context preview Snippet 1
... detect anomalies in Discovery schedules. • Type: true | false • Default value: false Warning: Turning off this feature significantly impacts the Discovery experience across multiple pages, charts, and tables. sn_disco_workspace.discovery_schedule_anomaly_detection_method Discovery Admin Workspace: Determines the approach used for anomaly detection in Discovery schedules. The machine learning (ML)-based approach uses historical scan data and AI algorithms to detect anomalies and outliers. ...

More in sn_disco_workspace

Other properties in the same namespace.

sn_disco_workspace.discovery_schedule_anomaly_detection_disabled The property controls whether anomaly detection is active for Discovery schedules, with a default setting that sn_disco_workspace.discovery_schedule_anomaly_discover_type This property configures the types of Discovery schedules, either IP-based or Cloud Discovery, for which anoma sn_disco_workspace.discovery_schedule_anomaly_ml_model_sensitivity It configures the sensitivity level for detecting anomalies in Discovery schedules, affecting how many anomali sn_disco_workspace.discovery_schedule_cis_discovered_anomaly_threshold This property determines the threshold percentage that triggers an anomalous status when the number of discove sn_disco_workspace.discovery_schedule_error_anomaly_threshold This property determines the percentage threshold for marking Discovery schedule error counts as anomalous bas sn_disco_workspace.discovery_schedule_provider_sync This property specifies whether syncing of the Provider value in discovery_schedule_extend_params is complete, sn_disco_workspace.discovery_schedule_runtime_anomaly_threshold This property determines the threshold percentage for marking a Discovery schedule as anomalous based on its e sn_disco_workspace.discovery_stats_time_scale This property sets the time frame for displaying Discovery schedule statistics in the Discovery Admin Workspac

All 15 properties in sn_disco_workspace →