Based on records of a large number of transactions, association rule learning is well-suited to be applied to the data that is routinely collected in day-to-day IT operations, enabling IT operations analytics tools to detect frequent patterns and identify critical changes. IT specialists need to see the big picture and understand, for example, how a problem on a database could impact an application server.

For a specific day, IT operations may take in a variety of alerts, presenting them in a transactional database. Using an association rule-learning algorithm, IT operations analytics tools can correlate and detect the frequent patterns of alerts appearing together. This can lead to a better understanding about how one component impacts another.

With identified alert patterns, it is possible to apply predictive analytics. For example, a particular database server hosts a web application and suddenly an alert about a database is triggered. By looking into frequent patterns identified by an association rule-learning algorithm, this means that the IT staff need to take action before the web application is impacted.

Association rule learning can also discover alert events originating from the same IT event. For example, every time a new user is added, six changes in the Windows operating system are detected. Next, in Application Portfolio Management (APM), IT may face multiple alerts, showing that the transactional time in a database as high. If all of these issues originate from the same source (such as getting hundreds of alerts about changes that are all due to a Windows update), this frequent pattern mining can help to quickly cut through a number of alerts, allowing the IT operators to focus on truly critical changes.