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The growing importance of log analysis and the management play an important role in the era of big data especially. With mass log information increasing, it's difficult to extract the needed information quickly, What's more, most cases are related information. Logstash can solve this problem suitably because of a built-in logging service.It can be combined with help from many log slaves to find data easily. Strictly speaking, Logstash alone are unable to rationalize and centralized the log files. This article integrated Logstash, Elasticsearch and Kibana into an organic whole, which is applied to an insurance business trading system LEK. It implements collection, query, polymerization and visual function of the transaction log, and form the unified convenient log management and analysis tool.

Community detection is an important research issue in complex network mining. In this paper, firstly, we define central nodes, called Extended Local Max-Degree ELMD nodes in a complex network. All the central nodes are used for the community expanding. We also prove that ELMD method is more precise and dispersed than local max-degree method in the real datasets. Secondly, we propose an improved local expansion method to expand community from the seeds (ELMD nodes), and this process is named as Extended Local Community Expansion with Modified R method (ELCEMR). ELCEMR is an unsupervised learning method, and does not need any priori-knowledge. Finally, the validations against the real-world datasets show that the proposed method performs better than other algorithms for community detection.