Combining Filtering and Cross-Correlation Efficiently for Streaming Time Series
Combining Filtering and Cross-Correlation Efficiently for Streaming Time Series
复制标题
有效结合滤波和互相关来处理流时间序列
DOI：
10.1145/3502738
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发表时间：
2022
影响因子：
3.6
通讯作者：
Mueen, Abdullah
中科院分区：
文献类型：
--
作者：
Zhong, Sheng;Souza, Vinicius M.;Mueen, Abdullah
监控系统具有数百或数千个分布式传感器，收集和传输实时流数据。这些系统中事件的早期检测，如地震监测系统中的地震，是预警生成等基本任务的基础。为了检测这样的事件，通常计算由传感器生成的不同信号之间的成对相关性。由于数据源（例如，传感器）在空间上是分离的，因此必须考虑信号之间的滞后相关性。此外，许多应用需要根据事件的类型来处理特定的频带，这要求在计算相关性之前进行滤波的预处理步骤。由于这些系统中的数据生成速度快且有大量传感器，因此滤波和滞后互相关的操作需要高效，以提供实时响应而不会丢失数据。本文提出了一种名为FilCorr的技术，可以在一个步骤中有效地计算这两个操作。我们通过保持滑动窗口上的频率变换来实现一个数量级的加速。我们的方法是准确的，没有敏感参数，易于并行化。除了我们的算法，我们还提供了一个公开的实时系统，名为Seisviz，采用FilCorr在其核心机制，监测地震仪网络。我们证明，我们的技术是适合于几个监测应用，如地震信号监测，运动监测和神经活动监测。
Monitoring systems have hundreds or thousands of distributed sensors gathering and transmitting real-time streaming data. The early detection of events in these systems, such as an earthquake in a seismic monitoring system, is the base for essential tasks as warning generations. To detect such events is usual to compute pairwise correlation across the disparate signals generated by the sensors. Since the data sources (e.g., sensors) are spatially separated, it is essential to consider the lagged correlation between the signals. Besides, many applications require to process a specific band of frequencies depending on the event’s type, demanding a pre-processing step of filtering before computing correlations. Due to the high speed of data generation and a large number of sensors in these systems, the operations of filtering and lagged cross-correlation need to be efficient to provide real-time responses without data losses. This article proposes a technique named FilCorr that efficiently computes both operations in one single step. We achieve an order of magnitude speedup by maintaining frequency transforms over sliding windows. Our method is exact, devoid of sensitive parameters, and easily parallelizable. Besides our algorithm, we also provide a publicly available real-time system named Seisviz that employs FilCorr in its core mechanism for monitoring a seismometer network. We demonstrate that our technique is suitable for several monitoring applications as seismic signal monitoring, motion monitoring, and neural activity monitoring.
DOI：
10.2307/j.ctt7zw8pz.4
发表时间：
2018-01
期刊：
Cerveau & Psycho
影响因子：
--
作者：
S. Dieguez
通讯作者：
S. Dieguez
影响因子：
3.4
作者：
Jackson, Nicko;Cole, Scott R.;Swann, Nicole C.
通讯作者：
Swann, Nicole C.