The enormous increase in the number of Internet of Things data sources (sensors, personal devices and embedded systems) has given rise to a huge amount of unnecessary and redundant data being sent to the cloud. This makes the task of processing and storing this volume of information a very hard one. Therefore, data pre-processing and filtering closer to data sources, such as in fog computing, is necessary. In particular, data reduction in the fog nodes may play a significant role in preventing the dramatic decrease in the Cloud of Things performance, especially in energy consumption, storage space, bandwidth and throughput. However, existing solutions are still lacking, considering they do not achieve the optimal data reduction performance especially in terms of delay and accuracy. In this article, we introduce an approach aiming to eliminate useless and redundant data captured by things basing on some intelligent information extraction techniques. We also evaluate the performance of the proposed solution on a real data set sample to demonstrate that it achieves a good features reduction.

中文翻译：

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将形式概念分析加入到物云数据修剪的特征提取中

物联网数据源（传感器、个人设备和嵌入式系统）数量的巨大增加导致大量不必要和冗余的数据被发送到云端。这使得处理和存储大量信息的任务变得非常困难。因此，在雾计算等更接近数据源的地方进行数据预处理和过滤是必要的。特别是，雾节点中的数据减少可能在防止物联网性能急剧下降方面发挥重要作用，尤其是在能源消耗、存储空间、带宽和吞吐量方面。然而，现有的解决方案仍然缺乏，考虑到它们没有达到最佳的数据减少性能，特别是在延迟和准确性方面。在本文中，我们介绍了一种基于一些智能信息提取技术的方法，旨在消除事物捕获的无用和冗余数据。我们还评估了所提出的解决方案在真实数据集样本上的性能，以证明它实现了良好的特征缩减。