Application Research Based On Sequence Pattern Mining In Network Alarm

As the network becomes large scale and its construction goes complex, it has become an important problem how to ensure the network run with high-effect and stabilization. Alarm correlation analysis is key issue for network management, which can assist network administrators filter useless alarm, delete redundancy alarm, orientate and forecast network fault, improves the efficiency of network management.In this thesis, we apply sequence pattern mining technology to network alarm correlation analysis and study alarm sequence pattern mining based on frequent pattern growth and network episode rules update etc important issues. The research and innovation are described in details as follows:1,Data pretreatment has deep influence on mining efficiency and result. Aming at the character of alarm data, we bring forward a data pretreatment model for translating the redundancy and noise original alarm data into alarm sequence database that is suitable for sequence pattern mining.2,Analyse alarm mining algorithm-FSPM-FP deeply which based on frequent pattern growth, owing to the problem of alarm sequence partial order doubtfully, an modification method for pattern tree construction is presented. At the same time, we also make some modifications in mining process, it can not only resolve this problem, moreover memory space of tree is been reduced.3,To deal with the problem that the single support-confidence condition to select frequent alarm sequence modes, bring forward a new mining algorithm-MNER-TP which based on network topology relationship. Due to the algorithm introduce the judgement of alarm sequence topology relationship, so it can filte high frequence but less relativity, and reserve high frequence and much more relativity alarm sequences, improve the precise of mining result.4,Research the updata frequent sequence pattern mining algorithm, then we bring forward an alarm update mining algorithm based on order pattern tree. It adopt an unification order for all the alarms so as to improve the efficiency of updating, which can avoid exchanging nodes continually in mining process.The algorithm is able to deal with support count change and alarm data renovate two kinds condition.