Department of Information and Communication Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Korea.
Department of Big Data, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Korea.
Sensors (Basel). 2018 Sep 13;18(9):3084. doi: 10.3390/s18093084.
As a large amount of stream data are generated through sensors over the Internet of Things environment, studies on complex event processing have been conducted to detect information required by users or specific applications in real time. A complex event is made by combining primitive events through a number of operators. However, the existing complex event-processing methods take a long time because they do not consider similarity and redundancy of operators. In this paper, we propose a new complex event-processing method considering similar and redundant operations for stream data from sensors in real time. In the proposed method, a similar operation in common events is converted into a virtual operator, and redundant operations on the same events are converted into a single operator. The event query tree for complex event detection is reconstructed using the converted operators. Through this method, the cost of comparison and inspection of similar and redundant operations is reduced, thereby decreasing the overall processing cost. To prove the superior performance of the proposed method, its performance is evaluated in comparison with existing methods.
随着物联网环境中传感器产生大量的流数据，已经开展了关于复杂事件处理的研究，以便实时检测用户或特定应用所需的信息。复杂事件是通过多个运算符将原始事件组合而成的。然而，由于现有复杂事件处理方法没有考虑运算符的相似性和冗余性，因此需要花费很长时间。在本文中，我们提出了一种新的实时处理来自传感器的流数据的复杂事件处理方法，该方法考虑了相似和冗余操作。在提出的方法中，将常见事件中的相似操作转换为虚拟运算符，将同一事件上的冗余操作转换为单个运算符。使用转换后的运算符重新构建用于复杂事件检测的事件查询树。通过这种方法，减少了相似和冗余操作的比较和检查成本，从而降低了整体处理成本。为了证明所提出方法的优越性能，将其性能与现有方法进行了比较评估。
