Performance issues and outages have significant impact on businesses due to the inadequacy of traditional monitoring methods. The complexity of underlying systems, the volume and variety of performance metrics collected and the desire to correlate unusual application logging have made it harder than ever to find problems and anomalous behavior before they impact business. Using statistical models, Bayesian methods and automated anomaly detection these problems can be solved faster and more accurately than before.
This 7 page academic paper demonstrates that:
Anomaly Detection in Application Performance Monitoring Data was written by Thomas Veasey and Stephen Dodson and a version of this paper was presented at ICMLC 2014 (6th International Conference on Machine Learning and Computing).