【摘要】 告警预测是保证整个网络的稳定性和可靠性的技术之一。现有的告警预测技术存在未考虑告警数据的时间顺序、难以获取先验知识等缺陷。由此,提出了一种基于拓扑约束的序列模式挖掘方法以发现有意义的告警序列模式。该方法主要考虑网络节点之间的拓扑连接关系,将其作为告警序列模式挖掘的约束条件;并且为了发现非频繁重大告警模式,改进了序列模式挖掘的剪枝操作,将包含重大告警的序列模式直接保留。实验结果表明,采用基于拓扑约束的序列模式挖掘方法挖掘出的告警序列模式可以提高网络告警预测的精度和效率,并能较准确地预测非频繁的"重大"告警。更多还原

【Abstract】 Alarm prediction is one of the techniques that ensures the stability and reliability of the entire network.Existing alarm forecasting technologies have defects such as not considering the time sequence of warning data and difficult to obtain the priori knowledge.Therefore,this paper proposed a sequence pattern mining method based on topological constraints to find a meaningful alarm sequence pattern.This algorithm mainly considers the topological connections between network nodes and takes them as constraints for mining the alarm sequence pattern.In order to find non-frequent major alarm mode,it improves pruning of sequential pattern mining,preserves sequence patterns containing major alarms directly.Experiments show that the alarm sequence mode mined by the sequential pattern mining method based on topological constraints can improve the accuracy and efficiency of the network alarm prediction and predict the infrequent"major"alarms more accurately.更多还原