Department of Computer Science, Blekinge Institute of Technology, 371 79 Karlskrona, Sweden.
iquest AB, Hägersten, 126 26 Stockholm, Sweden.
Sensors (Basel). 2021 Oct 12;21(20):6775. doi: 10.3390/s21206775.
In smart buildings, many different systems work in coordination to accomplish their tasks. In this process, the sensors associated with these systems collect large amounts of data generated in a streaming fashion, which is prone to concept drift. Such data are heterogeneous due to the wide range of sensors collecting information about different characteristics of the monitored systems. All these make the monitoring task very challenging. Traditional clustering algorithms are not well equipped to address the mentioned challenges. In this work, we study the use of MV Multi-Instance Clustering algorithm for multi-view analysis and mining of smart building systems' sensor data. It is demonstrated how this algorithm can be used to perform contextual as well as integrated analysis of the systems. Various scenarios in which the algorithm can be used to analyze the data generated by the systems of a smart building are examined and discussed in this study. In addition, it is also shown how the extracted knowledge can be visualized to detect trends in the systems' behavior and how it can aid domain experts in the systems' maintenance. In the experiments conducted, the proposed approach was able to successfully detect the deviating behaviors known to have previously occurred and was also able to identify some new deviations during the monitored period. Based on the results obtained from the experiments, it can be concluded that the proposed algorithm has the ability to be used for monitoring, analysis, and detecting deviating behaviors of the systems in a smart building domain.
在智能建筑中，许多不同的系统协同工作以完成其任务。在这个过程中，与这些系统相关的传感器以流的方式收集大量生成的数据，这些数据容易受到概念漂移的影响。由于收集有关被监控系统不同特征信息的传感器范围广泛，因此这些数据具有异构性。所有这些都使得监控任务变得极具挑战性。传统的聚类算法无法很好地应对上述挑战。在这项工作中，我们研究了使用 MV 多实例聚类算法对智能建筑系统传感器数据进行多视图分析和挖掘。展示了如何使用该算法对系统进行上下文和集成分析。本研究检查并讨论了算法可以用于分析智能建筑系统生成的数据的各种场景。此外，还展示了如何可视化提取的知识以检测系统行为中的趋势，以及如何帮助领域专家进行系统维护。在进行的实验中，所提出的方法能够成功检测到先前已知的异常行为，并且还能够在监测期间识别出一些新的异常。根据实验得到的结果，可以得出结论，所提出的算法具有用于监控、分析和检测智能建筑领域系统中异常行为的能力。
