Basic concepts

A few basic concepts are necessary when you use AIDA.

KPIs (Key Performance Indicators)
KPIs for HCL Workload Automation processes that are constantly monitored by AIDA. For example, the number of completed jobs in the current plan.

For more information about HCL Workload Automation KPIs managed by AIDA, see: KPIs for HCL Workload Automation.

Anomaly Source KPI
The KPI whose anomalous trend has triggered an alert.
For more information about how to analyze an anomaly source KPI, see: Analyzing an alert instance.
Correlated KPI
KPI correlated with the anomalous KPI. You can add one or more correlated KPIs to the anomaly data analysis.

For more information about how to add correlated KPIs to the anomaly data analysis, see: Analyzing an alert instance.

Data Point
Each singular observation of a KPI.
Anomaly
Unexpected KPI data point.

AIDA detects an anomaly when a KPI falls outside the expected range of values which is statistically defined based on KPI historical data.

For more information about anomaly data analysis, see Analyzing an alert instance.

Alert
Sequence of anomalies of a KPI.
An alert is defined by a set of parameters and conditions (see Alert trigger). When the conditions are met, the alert triggers, and an alert instance is created.
For example: 10 consecutive KPI data points that fall outside the expected range of values within 1 hour.

For more information, see: Alert definitions.

Alert Instance
A single occurrence of an alert, given its definition. As AIDA continuously monitors KPIs, when an alert is triggered, a record is created into OpenSearch database with the alert instance information.

For more information about alert instances, see: Overview dashboard.

Alert Severity
For each detected anomaly, AIDA calculates its percent deviation from the interval estimation. When an alert is generated, given its definition, the alert severity is calculated as average of percent deviations of the anomalies that concur to the alert generation. Alert severity classification by severity is:
  • High, when the average of percent deviations is > 30
  • Medium, when the average of percent deviations falls in the interval 20-30
  • Low, when the average of percent deviations is < 20
AIDA displays the highest severity of all the alerts in an alert instance.
Anomaly Bounds
The upper and lower bounds of the expected range of values for a KPI.
Alert Trigger
Set of conditions that define an alert. For example: 10 consecutive KPI data points that fall outside the expected range of values within 1 hour.

When triggering conditions are satisfied, a new alert instance is created inside OpenSearch database.

There are two types of alert triggers available for selection:
  • Continuous: Triggers when anomalous data points fall above or below the predicted range.
  • Total: Triggers for anomalous data points that are either above or below the predicted range, as well as those that exceed both thresholds.
Alerts are notified on the Workload Dashboard or via email.

For more information about receiving alert notifications, see: Receiving alert notifications.

Anomaly %
The percentage of observed KPI data points that fall outside the expected range of values in the reference time interval:
  • < 6 : Low
  • 6-10: Medium
  • >10: High
A KPI trend can show some anomalies, however an alert might not be issued if the trigger condition is not met.
Anomaly Data Analysis
Anomaly Data Analysis is part of AIDA User Interface. When anomalies in a KPI trend generate an alert, you can compare the anomalous trend with trends in one or more different time intervals. You can also add correlated KPIs to the anomaly data analysis to find root causes faster.

For more information about anomaly data analysis, see Analyzing an alert instance.

Alert Details
Alert Details provides detailed information about an alert, it's status, the current opened instances and its history.

For more information, see: Alert details.

Alert History
Calendar graph showing previous alert instances and related severity.

For more information, see: Alert details.

Timerange
How often a KPI is checked to detect anomalies (for example: every day, or every 10 minutes). It is set through the PROPHET_ORCHESTRATOR schedule_alert parameter of the common.env configuration file (or in the value.yaml file for Kubernetes deployments).
Special Days

Special days are days on which a KPI trend is affected by seasonality factors such as national holidays, vacation, business cycles, recurring events. To avoid false positive alerts, the special days are included in AIDA prediction model with a higher tolerance level than the standard days.

For more information, see: Managing special days.