High Impact Factor : 4.396 icon | Submit Manuscript Online icon |

Knowledge and Rule Based Learning Engine to Analyse the Logs for Troubleshooting


Prashant Achari , RV College of Engineering Bangalore; Susanta Adhikary, HP India Software Optns, India ; Mungara Jitendranath, RV College of Engineering Bangalore; Jayashree Madugundu, RV College of Engineering Bangalore


Log Analysis, Supervised learning, Rule engine, Data mining.


Technical support for software or hardware is always a challenge, both from the complexity and investment standpoint. Today the dependency on computers, networked systems and managing applications are becoming more dominant and only log files are available to trace the applications. Log file analysis process is heavily involved, in software, hardware as well as in network related domains. It serves for various purposes such as verifying the uniformity of the software functionality to the provided software specification, software performance check and troubleshooting the issues. Application log files or the logs generated by other monitoring tools are subjected to analysis for extracting information that can be vital in an investigation. These tasks demand competency to a great deal and are labor intensive when performed manually. There is no technique available to record expert knowledge and this stands as an obstacle to automate the analysis tasks. Hence there is a need for correlating information extracted from different locations in the same log file or multiple log files further adds to this complexity. This paper describes a practical approach for analysis of the logs using supervised learning to predict and to recommend steps for troubleshooting the issue. The overall solution proposed here is to automate the analysis of logs and provide recommendations for faster response to reported problems. The log analyser self-learns the new issue and provides necessary recommendations.

Other Details

Paper ID: IJSRDV2I4087
Published in: Volume : 2, Issue : 4
Publication Date: 01/07/2014
Page(s): 158-161

Article Preview

Download Article