A Nonparametric Adaptive Sampling Strategy for Online Monitoring of Big Data Streams
A Nonparametric Adaptive Sampling Strategy for Online Monitoring of Big Data Streams
复制标题
DOI：
10.1080/00401706.2017.1317291
复制
发表时间：
2018-01-01
期刊：
影响因子：
2.5
通讯作者：
Liu, Kaibo
中科院分区：
文献类型：
--
作者：
Xian, Xiaochen;Wang, Andi;Liu, Kaibo
随着传感器技术的快速发展，各种应用中产生了大量的数据，这对统计过程控制（SPC）提出了新的和独特的挑战。在这篇文章中，我们提出了一种非参数自适应采样（NAS）策略，以在线监测非正常的大数据流在有限的资源，其中只有一个子集的观察是在每个采集时间。特别是，该方法集成了一个基于秩的CRAMUM计划和一个创新的想法，纠正反秩统计与部分观测，它可以有效地检测到广泛的可能的均值漂移时，数据流是可交换的，并遵循任意分布。研究了过程受控和失控时NAS算法采样布局的两个理论性质。在不同的场景下进行了模拟和案例研究，以说明和评估所提出的方法的性能。本文的补充材料可在网上查阅。
With the rapid advancement of sensor technology, a huge amount of data is generated in various applications, which poses new and unique challenges for statistical process control (SPC). In this article, we propose a nonparametric adaptive sampling (NAS) strategy to online monitor nonnormal big data streams in the context of limited resources, where only a subset of observations are available at each acquisition time. In particular, this proposed method integrates a rank-based CUSUM scheme and an innovative idea that corrects the anti-rank statistics with partial observations, which can effectively detect a wide range of possible mean shifts when data streams are exchangeable and follow arbitrary distributions. Two theoretical properties on the sampling layout of the proposed NAS algorithm are investigated when the process is in control and out of control. Both simulations and case studies are conducted under different scenarios to illustrate and evaluate the performance of the proposed method. Supplementary materials for this article are available online.