Design and implementation for automated network troubleshooting using data mining
This work addresses network monitoring for mobile operators, but it appears incremental as it applies existing data mining techniques to a specific domain.
The paper tackled automated network troubleshooting in mobile networks by using data mining classifiers like decision trees and Bayesian methods to improve fault detection and isolation, concluding that the rules were highly effective.
The efficient and effective monitoring of mobile networks is vital given the number of users who rely on such networks and the importance of those networks. The purpose of this paper is to present a monitoring scheme for mobile networks based on the use of rules and decision tree data mining classifiers to upgrade fault detection and handling. The goal is to have optimisation rules that improve anomaly detection. In addition, a monitoring scheme that relies on Bayesian classifiers was also implemented for the purpose of fault isolation and localisation. The data mining techniques described in this paper are intended to allow a system to be trained to actually learn network fault rules. The results of the tests that were conducted allowed for the conclusion that the rules were highly effective to improve network troubleshooting.