面向发布/订阅分布式系统的运行模式识别算法

Wen-Jen Wu, Zhuowei Shen, Defeng Wang
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引用次数: 0

摘要

发布/订阅分布式系统通常用于关键应用程序。对其运行模式进行实时监控,及时发现异常状态是十分必要的。因此,识别正常运行模式是监视发布/订阅分布式系统的前提。基于Apriori算法,提出了一种用于发布/订阅分布式系统运行模式识别的加权频繁项集挖掘算法。通过引入事务矩阵,该算法只需扫描事务数据库一次。通过从影响和频率两个方面对事件进行加权,提高对出现次数少但重要性大的事件的支持度,从而挖掘出包含小频率事件的运行模式。实验结果表明,该算法能够有效地挖掘运行模式,性能优于Apriori算法和FP-growth算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Running Pattern Recognition Algorithm for Publish/Subscribe Distributed Systems
Publish/subscribe distributed systems are often used in critical applications. It is necessary to monitor their running patterns in real time to detect abnormal status. Therefore, identifying the normal running pattern is the precondition of monitoring publish/subscribe distributed systems. Based on Apriori algorithm, this paper presents a weighted frequent itemset mining algorithm for running pattern recognition of publish/subscribe distributed systems. By introducing the transaction matrix, the algorithm only needs to scan the transaction database once. By weighting the items from two aspects of influence and frequency, the support of the items with few occurrences but much importance can be improved, so that the running pattern containing small frequency events can be mined out. Experimental results show that the algorithm can effectively mine the running patterns, and has better performance than Apriori algorithm and FP-growth algorithm.
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