Networks have complex systems comprised of subsystem components like servers for various kinds of service and DCE (Data Communication Equipment) such as routers and switches. As each system operates constantly, it is hard to know exactly how the whole network operates. The network monitoring system, therefore, is required, in addition to monitoring and checking the network under steady operation, to detect system failures, weigh the situation and investigate into the cause. In this paper, we propose a method that we convert the three kinds of data, data by polling the server externally, data from computer resource of the server, and data from log information of the network server, into an integrated format, and then assemble them as generalized log format, so that we can extract the abnormal events without providing the monitoring system with any data such as key words, in advance. Firstly, we identify the patterns of frequency of the words by text-mining of the log information. Secondly, by means of signal processing of the frequency, periodically appeared patterns are figured out. Lastly, these patterns are associated with each other to presume the cause of the abnormal events. Our proposed method should support network administrators greatly in detecting system failures and investigating into the cause, and also reduce a lot of workload in monitoring log information.