Monitoring Sensor Measurement Anomalies of Streaming Environmental Data Using a Local Correlation Score

2013 Fourth International Conference on Computing for Geospatial Research and Application Pub Date : 2013-07-22 DOI:10.1109/COMGEO.2013.25

Ian Taylor, J. Sharp, David L. White, J. Hallstrom, G. Eidson, J. V. Oehsen, Edward B. Duffy, C. Privette, Charles T. Cook, Aravindh Sampath, G. Radhakrishnan

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Abstract

Real-time quality control (QC) of streaming natural resource data is needed to support the delivery of high quality data to system users. QC processes need to enable the identification of aberrations, as well as trends that may indicate degradation or component failures. These QC processes form a framework to support the goal of verified data delivered in a timely manner. In this paper, we investigate a method of computing Local Correlation Score (LCS) to detect anomalous patterns among sensor platforms in a concurrent manner. We use the R programming language and OpenMPI. Using empirical tests, we determine the benefits of computing the LCS in parallel, and on various sizes of clusters. We also analyze its use for real time mapping of Intelligent River data. Our results show that the LCS computed concurrently is an effective means for prompt quality assurance of natural resource data.

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利用局部相关评分监测传感器测量流环境数据的异常

为了向系统用户提供高质量的数据，需要对流自然资源数据进行实时质量控制。质量控制过程需要能够识别偏差，以及可能表明退化或组件失效的趋势。这些质量控制过程形成了一个框架，以支持及时交付经过验证的数据的目标。在本文中，我们研究了一种计算局部相关分数(LCS)的方法，以并发方式检测传感器平台之间的异常模式。我们使用R编程语言和OpenMPI。通过经验测试，我们确定了并行计算LCS的好处，以及在不同大小的集群上。我们还分析了它在智能河数据实时映射中的应用。结果表明，并行计算LCS是快速保证自然资源数据质量的有效手段。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2013 Fourth International Conference on Computing for Geospatial Research and Application

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