Anomaly

The specific parameter, metric or property where the anomaly was observed. Examples include: CPU usage percentage, API response time in milliseconds, HTTP error rate, memory utilization, network latency, transaction volume, etc. This helps identify the exact aspect of the system exhibiting anomalous behavior.

The type of analysis methodology used to detect the anomaly. This indicates how the anomaly was identified through different analytical approaches. Common types include: Frequency Analysis, Time Pattern Analysis, Volume Analysis, Sequence Analysis, Distribution Analysis, etc.

Details about the observed anomaly or observations that were flagged as anomalous compared to expected baseline behavior.

The specific pattern identified within the observation type. For Frequency Analysis, this could be 'FREQUENT', 'INFREQUENT', 'RARE', or 'UNSEEN'. For Time Pattern Analysis, this could be 'BUSINESS_HOURS', 'OFF_HOURS', or 'UNUSUAL_TIME'. For Volume Analysis, this could be 'NORMAL_VOLUME', 'HIGH_VOLUME', or 'SURGE'. The pattern values are specific to each observation type and indicate how the observed behavior relates to the baseline.

Group:context
The event data as received from the event source.

Group:primary
Unique identifier for the object

Data from the source that was not mapped into the schema.