Research on Algorithm of big data sensor anomaly diagnosis

Wenying Qiu, Weixi Gu
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Abstract

With the decrease of sensor cost and the increase of performance, the application of sensor is becoming more and more popular. Sensors in different locations and devices produce different data streams. We expect to analyze the data or events we are interested in in in real time from these data streams. With the increase of the number of sensors, a large number of sensors are organized into the form of networks. How to carry out data mining in sensor network data stream will bring a new challenge. As one of the most important data flow technologies, sliding window technology has been widely studied and applied. By accurately estimating the data distribution in the sliding window, we can carry out anomaly detection and other important applications.
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大数据传感器异常诊断算法研究
随着传感器成本的降低和性能的提高,传感器的应用越来越广泛。不同位置和设备的传感器产生不同的数据流。我们期望从这些数据流中实时分析我们感兴趣的数据或事件。随着传感器数量的增加,大量的传感器被组织成网络的形式。如何在传感器网络数据流中进行数据挖掘将会带来新的挑战。滑动窗口技术作为最重要的数据流技术之一,得到了广泛的研究和应用。通过准确估计滑动窗口中的数据分布,可以进行异常检测等重要应用。
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