网络日志数据中条件因果挖掘算法的优化研究

A conditional causality mining algorithm in network log data

网络操作中收集了大量的系统日志数据,找出精确的系统故障成为重要的研究方向。提出一种条件因果挖掘算法(CCMA),通过从日志消息中生成一组时间序列数据,分别用傅里叶分析和线性回归分析删除大量无关的周期性时间序列后,利用因果推理算法输出有向无环图,通过检测无环图的边缘分布,消除冗余关系得出最终结果。仿真结果表明,对比依赖挖掘算法(DMA)和网络信息关联与探索算法(NICE),CCMA算法在处理时间和边缘相关率2个主要性能指标方面均有改善,表明CCMA算法在日志事件挖掘中能有效优化处理速度和挖掘精度。

A large amount of system log data is collected during network operations,and finding out precise system faults has become an important research direction.This paper proposes a conditional causality mining algorithm(CCMA),which generates a set of time series data from log messages,and uses Fourier analysis and linear regression analysis to delete a large number of irrelevant periodic time series.Then,the causal inference algorithm is used to output the directed acyclic graph,and the final result is obtained by detecting the edge distribution of the acyclic graph and eliminating the redundant relationship.The simulation results show that the CCMA algorithm outperforms the dependent mining algorithm(DMA)and the network information correlation and exploration algorithm(NICE)in two main performance indicators such as processing time and edge correlation rate,which proves that the CCMA algorithm can effectively optimize the processing speed and mining accuracy in log event mining.