Abstract
In this paper, we propose a data mining based publish/subscribe system (DMPSS). First, the data mining technology is used to find attributes that are usually subscribed together, e.g. frequent itemset. Then subscriptions and events are installed by frequent itemsets contained in them. If subscriptions and events don’t contain any frequent itemset, they are delivered to specified RPs (rendezvous points) for matching. The usage of frequent itemsets provides two advantages to DMPSS. First, it achieves even matching load distribution on RPs. Second, it reduces the event publication cost. The performance of DMPSS is evaluated by simulations. The experimental results show that DMPSS realizes even matching load distribution, and it reduces the overhead for message transmission and latency dramatically.
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Song, J., Wang, H., Lv, P., Li, S., Xu, M. (2015). A Data Mining Based Publish/Subscribe System over Structured Peer-to-Peer Networks. In: Lee, R. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. Studies in Computational Intelligence, vol 569. Springer, Cham. https://doi.org/10.1007/978-3-319-10389-1_1
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DOI: https://doi.org/10.1007/978-3-319-10389-1_1
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