Log and execution trace analytics system

Log files are available on every computer system. They automatically record important run time events of operating systems or software applications. They are mainly used to find the root cause of failures. Analyzing such log files allows us to detect anomalies, problems and improve the system. Since the log files are usually unstructured or semi-structured, the important task of log analysis is to parse usually immense amount of log message strings into the human readable and actionable reports. In this paper, we propose an implementation of a machine learning based log parsing system using Named Entity Recognition which is the process of identifying and categorizing entities in the text. Our approach makes use of Bidirectional Encoder Representations from Transformers (BERT) to extract entities. The paper reports the results of experiments on two benchmark; macOS and Linux OS datasets. © 2021 IEEE