Name
Abstract | ||
|---|---|---|
Analyzing the log file for software or device provides a focal point for making incremental improvements; it is the performed step to start the incident analysis. Although, log messages format or contents may not always be fully documented, and described in many different formats. It makes the log analysis task more difficult, affects the correction deadline of incidents and therefore involves a high financial risk.
In this paper, we survey the log file analysis and the existing systems elaborated to resolve current issue. Then, we propose a methodology to support the log analysis in the complex environment. The KN-K-Nearest-Neighbor (KNN) classification method was chosed to be used online by weka to predict the error. Therefore, a program was developed in python to extract, clean and format the log file before comparing the different algorithms of the classifiation method KNN, J48 and Bayes - NaiveBayes in the context of dataset.API was used in order to process Weka.
Finally, we illustrate our proposal in the Tivoli Storage Manager (TSM) file log and provide a description of the results obtained.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3368756.3369069 | Proceedings of the 4th International Conference on Smart City Applications |
Keywords | DocType | ISBN |
Bayes-Naïve Bayes, J48, KNN, TSM, classification, clustering, data analysis, data mining, knowledge extraction | Conference | 978-1-4503-6289-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. El Abdelkhalki | 1 | 0 | 0.34 |
| Mohamed Ben Ahmed | 2 | 195 | 45.34 |
| A. Slimani | 3 | 0 | 0.34 |