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.