Abstract
With deployments of complicated or complex large-scale micro-service architectures the kind of data generated from all those systems makes a typical production infrastructure huge, complicated and difficult to manage. In this scenario, logs play a major role and can be considered as an important source of information in a large-scale secured environment. Till date, many researchers have contributed various methods towards conversion of unstructured logs to structured ones. However, post conversion, the dimension of the dataset generated increases many folds which are too complex for data analysis. In this paper, we have discussed techniques and methods to deal with extraction of all features from a produced structured log, reducing N-dimensional features to fixed dimensions without compromising the quality of data in a cost-efficient manner that can be used for any further machine learning-based analysis.
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