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An efficient complex event detection model for high proportion disordered RFID event stream

机译:高比例无序RFID事件流的高效复杂事件检测模型

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With the aim of solving the detection problems for current complex event detection models in detecting a related event for a complex event from the high proportion disordered RFID event stream due to its big uncertainty arrival, an efficient complex event detection model based on Extended Nondeterministic Finite Automaton (ENFA) is proposed in this paper. The achievement of the paper rests on the fact that an efficient complex event detection model based on ENFA is presented to successfully realize the detection of a related event for a complex event from the high proportion disordered RFID event stream. Specially, in our model, we successfully use a new ENFA-based complex event detection model instead of an NFA-based complex event detection model to realize the detection of the related events for a complex event from the high proportion disordered RFID event stream by expanding the traditional NFA-based detection model, which can effectively address the problems above. The experimental results show that the proposed model in this paper outperforms some general models in saving detection time, memory consumption, detection latency and improving detection throughput for detecting a related event of a complex event from the high proportion out-of-order RFID event stream.
机译:为了解决当前复杂事件检测模型的检测问题,以从由于不确定性到来而导致的高比例无序RFID事件流中检测复杂事件的相关事件,基于扩展不确定性有限自动机的高效复杂事件检测模型(ENFA)在本文中提出。本文的成就基于以下事实:提出了一种基于ENFA的有效复杂事件检测模型,以成功实现从高比例无序RFID事件流中检测复杂事件的相关事件。特别地,在我们的模型中,我们成功地使用新的基于ENFA的复杂事件检测模型,而不是基于NFA的复杂事件检测模型,通过扩展扩展来实现从高比例无序RFID事件流中检测复杂事件的相关事件。传统的基于NFA的检测模型,可以有效解决上述问题。实验结果表明,该模型在从高比例无序RFID事件流中检测复杂事件的相关事件方面,在节省检测时间,内存消耗,检测等待时间和提高检测吞吐量方面优于某些通用模型。 。

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