A Machine Learning Approach to Database Failure Prediction

Abstract

In this study, we apply machine learning algorithms to predict technical failures that can be encountered in Oracle databases and related services. In order to train machine learning algorithms, data from log files are collected hourly from Oracle database systems and labeled with two classes; normal or abnormal. We use several data science approaches to preprocess and transform the input data from raw format to the format, which can be feed to the algorithms. After the preprocessing, several different machine learning classifiers are trained and evaluated on our datasets. Our results show that warnings that lead to failures which is dubbed as abnormal events can be predicted using supervised machine learning algorithms, in particular, the Random Forest algorithm, with a relatively satisfactory Recall (75.7%) and Precision (84.9%) which is visibly higher than the other classifiers.