ELM based approximate dynamic cycle matching for homogeneous symmetric Pub/Sub system

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• 作者：Botao Wang (1) (2)
  Pingping Liu (1) (2)
  Guoren Wang (1) (2)
  Xiangguo Zhao (1) (2)
  
  1. College of Information Science and Engineering ; Northeastern University ; Liaoning ; 110004 ; Shenyang ; China
  2. Key Laboratory of Medical Image Computing (Northeastern University) ; Ministry of Education ; Liaoning ; China
  
• 关键词：Publish/subscribe ; Approximate cycle matching ; Classification ; Extreme learning machine
• 刊名：World Wide Web
• 出版年：2015
• 出版时间：March 2015
• 年：2015
• 卷：18
• 期：2
• 页码：265-280
• 全文大小：696 KB
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• 刊物类别：Computer Science
• 刊物主题：Information Systems Applications and The Internet
  Database Management
  Operating Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-1413

文摘

The number of cycle matchings increases exponentially with the number of subscriptions and the maximum length of cycle matchings, which needs a large amount of space to store intermediate results. Approximate cycle matching aims to store only a small part of intermediate results and find cycle matchings as many as possible. The existing solution prunes the intermediate results by a threshold of probability of a subscription to be matched, where the discrete degree of probabilities is neglected. In this paper, we propose an approximate dynamic cycle matching algorithm based on intermediate results classification using extreme learning machine. We first introduce a method of incorporating probability information into feature vector, and then propose the approximate cycle algorithm. Further, we propose a dynamic classification strategy considering that the data distribution of subscriptions may change as time goes on. The proposed approximate cycle matching algorithm and the dynamic classification strategy are evaluated in a simulated environment. The results show that compared with the approximate cycle matching based on probability threshold, the approximate cycle matching based on ELM classification is faster, and the dynamic classification strategy is more efficient and convenient. ELM is more suitable for approximate dynamic cycle matching than SVM with regards to response time.