Intelligent Search of Correlated Alarms for GSM Networks with Model-based Constraints

TL;DR

This paper proposes a model-based constrained data mining algorithm, SMC, for efficiently discovering correlated alarms in GSM networks, significantly reducing search time by incorporating network architecture constraints.

Contribution

It introduces a novel SMC algorithm that integrates network model constraints into alarm correlation analysis, enhancing efficiency over unconstrained methods.

Findings

SMC with constraints is twice as fast as unconstrained algorithms.

Network model constraints improve alarm correlation detection efficiency.

Experimental results validate the effectiveness of the proposed approach.

Abstract

In order to control the process of data mining and focus on the things of interest to us, many kinds of constraints have been added into the algorithms of data mining. However, discovering the correlated alarms in the alarm database needs deep domain constraints. Because the correlated alarms greatly depend on the logical and physical architecture of networks. Thus we use the network model as the constraints of algorithms, including Scope constraint, Inter-correlated constraint and Intra-correlated constraint, in our proposed algorithm called SMC (Search with Model-based Constraints). The experiments show that the SMC algorithm with Inter-correlated or Intra-correlated constraint is about two times faster than the algorithm with no constraints.