Alarm Correlation Analysis Based On Data Mining

In recent years, as the rapid development of telecommunications and the expanding of the scale and complexity of the telecommunication networks, telecommunication networks produce daily large amounts of alarm data. Because of the large volumes of alarm data and the highly fragmented alarm information, once a fault occurs in the networks, it’s difficult for administrators to find valuable information facing the large volumes of alarms. As a result, it is very hard for administrators to rapidly identify, correct and predict network problems. After dealing with the problems, there is not any valuable information remained. In order to deal with the problem intelligently, introducing the alarm correlation analysis is a good choice.The paper studied alarm correlation analysis based on data mining, introduced the classic association rules and sequence rules, preprocessed the original alarm data, designed and implemented data mining models to mine alarm data provided by a mobile telecommunication company.The main works and contributions in this paper are as follows:Preprocessing the alarm data. The data preprocess are divided into two parts:firstly, by analyzing the feature of the alarm data, the paper designed reasonable alarm filtering mechanism and implementation methods, so as to reduce the number of alarms and improve the efficiencies. Secondly, the paper designed, implemented and optimized the mechanism of sliding time window, transforming the alarm data into transactions required by data mining models.The paper analyzed alarm correlation, alarm data mining, introduced and compared several association rules and sequence rules algorithms.We designed models of association rules and sequence rules respectively, implemented and adjusted the models using data mining tool-SPSS Clementine. By using the models, we mined the alarms and got the rules in the form of graphs and tables.The paper analyzed the rules obtained from data mining and verified the correctness of the rules and the value of alarm failure analysisThe significance of this paper is to study a large number of the original alarm information from network alarms to get the association rules and sequence rules, providing a viable solution for the analysis of the communication network alarm information. Mining results can help to analyze and locate the network problems better, to achieve the forecast of future network equipment failure with strong application. Integrating data mining to the model process provides a typical example of a combination of theory and practice.