Multi-View Data Analysis Techniques for Monitoring Smart Building Systems
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/20/6775 |
| _version_ | 1797513192336785408 |
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| author | Vishnu Manasa Devagiri Veselka Boeva Shahrooz Abghari Farhad Basiri Niklas Lavesson |
| author_facet | Vishnu Manasa Devagiri Veselka Boeva Shahrooz Abghari Farhad Basiri Niklas Lavesson |
| author_sort | Vishnu Manasa Devagiri |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:13:12Z |
| format | Article |
| id | doaj.art-5056ced33c964da493506024e6f25353 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T06:13:12Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5056ced33c964da493506024e6f253532023-11-22T19:57:16ZengMDPI AGSensors1424-82202021-10-012120677510.3390/s21206775Multi-View Data Analysis Techniques for Monitoring Smart Building SystemsVishnu Manasa Devagiri0Veselka Boeva1Shahrooz Abghari2Farhad Basiri3Niklas Lavesson4Department of Computer Science, Blekinge Institute of Technology, 371 79 Karlskrona, SwedenDepartment of Computer Science, Blekinge Institute of Technology, 371 79 Karlskrona, SwedenDepartment of Computer Science, Blekinge Institute of Technology, 371 79 Karlskrona, Swedeniquest AB, Hägersten, 126 26 Stockholm, SwedenDepartment of Software Engineering, Blekinge Institute of Technology, 371 79 Karlskrona, SwedenIn 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.https://www.mdpi.com/1424-8220/21/20/6775evolutionary clusteringmulti-view clusteringmulti-instance learningclosed patternsstreaming dataformal concept analysis |
| spellingShingle | Vishnu Manasa Devagiri Veselka Boeva Shahrooz Abghari Farhad Basiri Niklas Lavesson Multi-View Data Analysis Techniques for Monitoring Smart Building Systems Sensors evolutionary clustering multi-view clustering multi-instance learning closed patterns streaming data formal concept analysis |
| title | Multi-View Data Analysis Techniques for Monitoring Smart Building Systems |
| title_full | Multi-View Data Analysis Techniques for Monitoring Smart Building Systems |
| title_fullStr | Multi-View Data Analysis Techniques for Monitoring Smart Building Systems |
| title_full_unstemmed | Multi-View Data Analysis Techniques for Monitoring Smart Building Systems |
| title_short | Multi-View Data Analysis Techniques for Monitoring Smart Building Systems |
| title_sort | multi view data analysis techniques for monitoring smart building systems |
| topic | evolutionary clustering multi-view clustering multi-instance learning closed patterns streaming data formal concept analysis |
| url | https://www.mdpi.com/1424-8220/21/20/6775 |
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