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- Devagiri, Vishnu ManasaBlekinge Tekniska Högskola,Institutionen för datavetenskap (author)
Multi-view data analysis techniques for monitoring smart building systems
- Article/chapterEnglish2021
Publisher, publication year, extent ...
- 2021-10-12
- MDPI,2021
- electronicrdacarrier
Numbers
- LIBRIS-ID:oai:DiVA.org:bth-22225
- https://urn.kb.se/resolve?urn=urn:nbn:se:bth-22225URI
- https://doi.org/10.3390/s21206775DOI
Supplementary language notes
- Language:English
- Summary in:English
Part of subdatabase
- SwePubSwePub
Classification
Notes
- open access
- 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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Subject headings and genre
- NATURVETENSKAP Data- och informationsvetenskap Datavetenskap hsv//swe
- NATURAL SCIENCES Computer and Information Sciences Computer Sciences hsv//eng
- Closed patterns
- Evolutionary clustering
- Formal concept analysis
- Multi-instance learning
- Multi-view clustering
- Smart buildings
- Streaming data
- Buildings
- Clustering algorithms
- Building systems
- Closed pattern
- Concept drifts
- Data analysis techniques
- Large amounts of data
- Multi-views
Added entries (persons, corporate bodies, meetings, titles ...)
- Boeva, Veselka,ProfessorBlekinge Tekniska Högskola,Institutionen för datavetenskap(Swepub:bth)vbx (author)
- Abghari, ShahroozBlekinge Tekniska Högskola,Institutionen för datavetenskap(Swepub:bth)sab (author)
- Basiri, Fahradiquest AB, SWE (author)
- Lavesson, Niklas,Professor,1976-Blekinge Tekniska Högskola,Institutionen för programvaruteknik(Swepub:bth)nla (author)
- Blekinge Tekniska HögskolaInstitutionen för datavetenskap (creator_code:org_t)
Related titles
- In:Sensors: MDPI21:201424-8220
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