An efficient event matching system for semantic smart data in the Internet of Things (IoT) environment
An efficient event matching system for semantic smart data in the Internet of Things (IoT) environment
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DOI：
10.1016/j.future.2018.12.064
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发表时间：
2019-06-01
影响因子：
7.5
通讯作者：
Fortino，Giancarlo
中科院分区：
文献类型：
--
作者：
Alhakbani，Noura;Hassan，Mohammad Mehedi;Fortino，Giancarlo
发布/订阅通信模型支持异构方之间的通信，因此被证明是最适合物联网环境的通信模型。标准或通用发布/订阅使用精确模型将事件与订阅进行匹配。然而，在物联网环境中，精确匹配是一项极端的要求，因为环境多样且规模庞大，并且需要生成各种形式的智能数据。因此，必须考虑语义相似的事件并将其作为可能的匹配返回给订阅者。然而，将事件与订阅大致匹配是一项复杂得多的任务，这会对匹配效率产生负面影响。我们提出的基于树结构的语义匹配算法(SMT)可以提供高效的通信来支持时间关键型应用。SMT在吞吐量方面实现了线性时间，而在以前的工作中实现了指数时间。将SMT与分类聚类相结合提高了F-Score的有效性，F-Score是结果召回率和精确度的指标，特别是在100%的订阅要进行语义匹配的情况下。
The publish/subscribe model for communication has proved to be the most suitable in the Internet of things (IoT) environment because of the decoupling provided by this model that supports communication among heterogeneous parties. The standard or common publish/subscribe uses exact model to match events to subscriptions. However, in the IoT environment, an exact match is an extreme requirement because of the diverse and large environment and generation of various forms of Smart data. Therefore, semantically similar events must be considered and returned to subscribers as a possible match. However, matching events approximately to subscriptions is a much more complex task that negatively affects the efficiency of matching. Our proposed algorithm, semantic matching using the tree structure (SMT), provides efficient communication to support time-critical applications. SMT achieved linear time in terms of throughput compared with exponential time achieved in previous work. Combining SMT with taxonomy clustering improved the effectiveness in terms of the F-score, which is an indication of the recall and precision of the results, particularly when 100% of subscriptions were to be semantically matched.