A must-have requirement from an AIOps platform is event/alert correlation. Data aggregated from multiple external tools, are processed using the correlation engine. Correlation happens in real time and unsupervised machine learning algorithms are used in correlation.
Data from various sources are ingested into ZIF in real time either by push or pull mechanism. As the data is ingested, labelling algorithms are run on the data to label the data based on identifiers. The labelled data is passed through the correlation engine where unsupervised algorithms are run to mine the patterns from the labelled data. Sub-sequence mining algorithms help in identifying unique patterns from the data.
Unique patterns identified are clustered using clustering algorithms to form cases. Every case that is generated is marked by unique a case id. As part of the clustering process, seasonality are checked from the historical transactions to derive a higher accuracy on correlation.
Correlation done based on pattern recognition helps in eliminating the need of relational CMDB from enterprise. The accuracy of the correlation increases as pattern reoccur. Algorithms also have the capability to unlearn patterns based on the feedback that can be provided by actions taken on correlation.
Algorithms used being unsupervised, the patterns are learned by the platform with zero human intervention post implementation.
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