机器学习辅助收集减少的传感器数据,改进分析管道

Ankur Verma , Ayush Goyal , Soundar Kumara
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引用次数: 0

摘要

传感器数据正在越来越多地提供更好的运营可视性。然而,数据洪流也给数据分析管道带来了成本和复杂性方面的挑战,数据分析管道包括边缘计算、电源、传输和存储,用于数据驱动决策。为解决数据泛滥问题,我们提出了一种机器学习辅助方法,即在前期收集较少的数据,以解决不同的传感器数据分析问题。在以奈奎斯特速率采样的同时,我们并不收集每个数据点,而是根据信号中的信息含量进行采样。我们进行了全面的实验设计,结果表明,收集超过一定数量的原始数据只能带来微不足道的性能提升。我们对所提出的近实时方法的工程优势进行了量化,结果显示,工业数字化转型应用所需的分析管道资源显著减少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning-assisted collection of reduced sensor data for improved analytics pipeline

Sensor data is increasingly offering better operational visibility. However, the data deluge is also posing cost and complexity challenges on the data analytics pipeline, which comprises of edge computing, power, transmission, and storage for data-driven decision making. To address the data deluge problem, we propose a machine learning assisted approach of collecting less data upfront to solve different sensor data analytics problems. While sampling at Nyquist rates, we do not collect every data point, but rather sample according to the information content in the signal. A comprehensive experimental design is undertaken to show that collecting more than a certain fraction of raw data only leads to infinitesimal performance improvements. The engineering advantages of the proposed near real-time approach are quantified showing a significant reduction in analytics pipeline resources required for industrial digital transformation applications.

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