Institute of Computer Science, Warsaw University of Technology, 00-665 Warsaw, Poland.
Sensors (Basel). 2021 Oct 27;21(21):7128. doi: 10.3390/s21217128.
In many embedded systems, we face the problem of correlating signals characterising device operation (e.g., performance parameters, anomalies) with events describing internal device activities. This leads to the investigation of two types of data: time series, representing signal periodic samples in a background of noise, and sporadic event logs. The correlation process must take into account clock inconsistencies between the data acquisition and monitored devices, which provide time series signals and event logs, respectively. The idea of the presented solution is to classify event logs based on the introduced similarity metric and deriving their distribution in time. The identified event log sequences are matched with time intervals corresponding to specified sample patterns (objects) in the registered signal time series. The matching (correlation) process involves iterative time offset adjustment. The paper presents original algorithms to investigate correlation problems using the object-oriented data models corresponding to two monitoring sources. The effectiveness of this approach has been verified in power consumption analysis using real data collected from the developed Holter device. It is quite universal and can be easily adapted to other device optimisation problems.
在许多嵌入式系统中，我们面临着将描述设备操作的信号（例如性能参数、异常）与描述内部设备活动的事件相关联的问题。这导致了对两种类型的数据的研究：时间序列，它表示在噪声背景下的信号周期性样本，以及偶尔出现的事件日志。相关过程必须考虑到数据采集和被监测设备之间的时钟不一致性，这两个设备分别提供时间序列信号和事件日志。所提出解决方案的思路是基于引入的相似性度量标准对事件日志进行分类，并推导出它们在时间上的分布。识别出的事件日志序列与在注册的信号时间序列中指定样本模式（对象）对应的时间间隔相匹配。匹配（相关）过程涉及迭代时间偏移调整。本文提出了使用与两个监测源相对应的面向对象数据模型来研究相关问题的原始算法。该方法已在使用从开发的 Holter 设备收集的实际数据进行功耗分析中得到验证。它具有相当的通用性，可以很容易地适应其他设备优化问题。
