Most Oracle performance alerting is based on simple rules. For sure, some statistics are used, but still it's based on simple rules.
With machine learning (ML) we easily go beyond simple rules, because ML algorithms are built to recognize patterns in data. The pattern recognition ability of ML goes far beyond what even a highly trained performance expert could ever hope to achieve.
However, most monitoring and alerting systems do not truly use ML, many IT organizations are not ready to embrace ML and many DBAs are not cross-trained in ML.
A novel solution to this problem is to use ML to create a list of rules that define "poor performance" based on your real system and real users. These rules may not even make sense to us, but they will take your alerting to a higher level because they are based on your data and ML algorithms.
Join me for a fascinating presentation about how to use the results of ML as inputs into your existing monitoring and alerting systems.
