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Article

Multi-View Data Analysis Techniques for Monitoring Smart Building Systems

1
Department of Computer Science, Blekinge Institute of Technology, 371 79 Karlskrona, Sweden
2
iquest AB, Hägersten, 126 26 Stockholm, Sweden
3
Department of Software Engineering, Blekinge Institute of Technology, 371 79 Karlskrona, Sweden
*
Author to whom correspondence should be addressed.
Academic Editors: Mohamed Mosbah, Imen Jemili and Mohamed Tounsi
Sensors 2021, 21(20), 6775; https://doi.org/10.3390/s21206775
Received: 12 August 2021 / Revised: 24 September 2021 / Accepted: 1 October 2021 / Published: 12 October 2021
(This article belongs to the Special Issue Distributed Algorithms for Wireless Sensor Networks)
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. View Full-Text
Keywords: evolutionary clustering; multi-view clustering; multi-instance learning; closed patterns; streaming data; formal concept analysis; smart buildings evolutionary clustering; multi-view clustering; multi-instance learning; closed patterns; streaming data; formal concept analysis; smart buildings
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MDPI and ACS Style

Devagiri, V.M.; Boeva, V.; Abghari, S.; Basiri, F.; Lavesson, N. Multi-View Data Analysis Techniques for Monitoring Smart Building Systems. Sensors 2021, 21, 6775. https://doi.org/10.3390/s21206775

AMA Style

Devagiri VM, Boeva V, Abghari S, Basiri F, Lavesson N. Multi-View Data Analysis Techniques for Monitoring Smart Building Systems. Sensors. 2021; 21(20):6775. https://doi.org/10.3390/s21206775

Chicago/Turabian Style

Devagiri, Vishnu M., Veselka Boeva, Shahrooz Abghari, Farhad Basiri, and Niklas Lavesson. 2021. "Multi-View Data Analysis Techniques for Monitoring Smart Building Systems" Sensors 21, no. 20: 6775. https://doi.org/10.3390/s21206775

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