DataMiner Analytics tutorials

DataMiner Analytics, also known as DataMiner Augmented Operations, leverages state-of-the-art big data and artificial intelligence technology for several features, including behavioral anomaly detection, automatic incident tracking, and more.

Tutorials

Name	Description
Detecting anomalies with DataMiner	Get to know DataMiner's anomaly detection features and leverage them for alarm monitoring.
Improving anomaly detection using feedback	Tailor DataMiner's anomaly detection feature to your needs using feedback.
Staying ahead of issues with proactive cap detection	Use DataMiner's proactive cap detection features to get notified about potential upcoming issues.
Gaining insights using time-scoped relation learning	Use DataMiner's time-scoped relation learning features to find the root cause of several behavioral anomalies.
Working with trend patterns in DataMiner Cube	Use DataMiner's pattern matching feature to add context information to trend graphs.
Creating an anomaly overview dashboard	Create a dashboard that shows an overview of behavioral change event data.
Using trend patterns to detect backup failures	Create a dashboard that allows you to check whether recent backups have occurred, based on trend patterns.
Fine-tuning incident tracking in your system	Learn how to enable, disable, tweak, and add rules for alarm grouping.