AWS AmazonCloudWatch documentation change
Summary
Updated multiple instances of 'outlier detection' to 'anomaly detection' in titles, navigation, feature descriptions, and explanatory text.
Security assessment
This is a widespread terminology update. The change describes the same machine learning-based monitoring feature with updated naming. No security vulnerability or new security feature is mentioned.
Diff
diff --git a/AmazonCloudWatch/latest/monitoring/appinsights-tutorial-sap-hana.md b/AmazonCloudWatch/latest/monitoring/appinsights-tutorial-sap-hana.md index 98e6bfc0f..0427c072d 100644 --- a//AmazonCloudWatch/latest/monitoring/appinsights-tutorial-sap-hana.md +++ b//AmazonCloudWatch/latest/monitoring/appinsights-tutorial-sap-hana.md @@ -5 +5 @@ -Supported environmentsSupported operating systemsFeaturesPrerequisitesSet up monitoringManage monitoringTroubleshooting problems detectedDetecting outliersTroubleshooting Application Insights +Supported environmentsSupported operating systemsFeaturesPrerequisitesSet up monitoringManage monitoringTroubleshooting problems detectedAnomaly detectionTroubleshooting Application Insights @@ -27 +27 @@ This tutorial demonstrates how to configure CloudWatch Application Insights to s - * Detecting outliers + * Anomaly detection @@ -102 +102 @@ CloudWatch Application Insights for SAP HANA provides the following features: - * Automatic SAP HANA alarm creation based on outlier detection + * Automatic SAP HANA alarm creation based on anomaly detection @@ -347 +347 @@ The log group widget in the problem dashboard shows the `ACCESS DENIED` event. T -## Detecting outliers for SAP HANA +## Anomaly detection for SAP HANA @@ -349 +349 @@ The log group widget in the problem dashboard shows the `ACCESS DENIED` event. T -For specific SAP HANA metrics, such as the number of thread count, CloudWatch applies statistical and machine learning algorithms to define the threshold. These algorithms continuously analyze the metrics of the SAP HANA database, determine normal baselines, and surface anomalies with minimal user intervention. The algorithms generate an outlier detection model, which generates a range of expected values that represent normal metric behavior. +For specific SAP HANA metrics, such as the number of thread count, CloudWatch applies statistical and machine learning algorithms to define the threshold. These algorithms continuously analyze the metrics of the SAP HANA database, determine normal baselines, and surface anomalies with minimal user intervention. The algorithms generate an anomaly detection model, which generates a range of expected values that represent normal metric behavior. @@ -351 +351 @@ For specific SAP HANA metrics, such as the number of thread count, CloudWatch ap -Detecting outliers algorithms account for the seasonality and trend changes of metrics. The seasonality changes can be hourly, daily, or weekly, as shown in the following examples of the SAP HANA CPU usage. +Anomaly detection algorithms account for the seasonality and trend changes of metrics. The seasonality changes can be hourly, daily, or weekly, as shown in the following examples of the SAP HANA CPU usage. @@ -355 +355 @@ Detecting outliers algorithms account for the seasonality and trend changes of m -After you create a model, CloudWatch outlier detection continuously evaluates the model and makes adjustments to it to ensure that is it as accurate as possible. This includes retraining the model to adjust if the metric values evolve over time or experience sudden changes. It also includes predictors to improve the models for metrics that are seasonal, spiky, or sparse. +After you create a model, CloudWatch anomaly detection continuously evaluates the model and makes adjustments to it to ensure that is it as accurate as possible. This includes retraining the model to adjust if the metric values evolve over time or experience sudden changes. It also includes predictors to improve the models for metrics that are seasonal, spiky, or sparse.