Generic visual data mining-based framework for revealing abnormal operation patterns in building energy systems

Zhang, Chaobo; Zhao, Yang; Li, Tingting; Zhang, Xuejun; Adnouni, Meriem

doi:10.1016/j.autcon.2021.103624

Cited by 10 publications

(4 citation statements)

References 44 publications

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“…As stated before, visualization increases the understandability of complex results. In [32], the authors highlight the need for continuous visualization in a data mining process. Inspired by this, we propose suitable visualizations of the results obtained in each phase of the MV-MIC algorithm.…”

Section: Visualization Of Resultsmentioning

confidence: 99%

“…The amount of data generated by the smart building systems is enormous and difficult to interpret; this can be addressed using visual data mining [32]. Visualization can be considered as a part of data mining and knowledge discovery [33].…”

Section: Data Visualization and Analysismentioning

confidence: 99%

“…Visualization can be considered as a part of data mining and knowledge discovery [33]. It helps in representation and eases the task of understanding a huge volume of data that are difficult to present and analyze in a textual form [32,34]. One of the significant advantages of visual exploration, as stated by Keim [34] is that it is capable of handling heterogeneous and noisy data better than what statistical or ML techniques have to offer.…”

Section: Data Visualization and Analysismentioning

confidence: 99%

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Multi-View Data Analysis Techniques for Monitoring Smart Building Systems

Devagiri

Boeva

Abghari

et al. 2021

Sensors

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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.

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Section: Visualization Of Resultsmentioning

confidence: 99%

Section: Data Visualization and Analysismentioning

confidence: 99%

Section: Data Visualization and Analysismentioning

confidence: 99%

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Multi-View Data Analysis Techniques for Monitoring Smart Building Systems

Devagiri

Boeva

Abghari

et al. 2021

Sensors

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“…It aims to help decision making by using the method of computer learning (Koyuncugil & Özgülbaş, 2016) on the basis of these concepts (Zhang et al, 2021). For example, the classification process, which Çalışan Performansı Ölçeğindeki İfadelerin Karar Ağacı Algoritması ile Belirlenmesi -Determining The Expressions on The Employee Performance Scale With The Decision Tree Algorithm Sümeyye ÇELİK, Özlem ÇETINKAYA BOZKURT, Nisa EKŞİLİ is among the data mining methods, is the automatic classification process of new samples with the help of the model obtained from the data with a supervised approach (Maliah & Shani, 2021).…”

Section: Methodsmentioning

confidence: 99%

Determining the Expressions on the Employee Performance Scale With the Decision Tree Algorithm

Çelik

Bozkurt

EKŞİLİ³

2022

Mehmet Akif Ersoy Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi

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Çalışan performansı birçok çalışmaya konu olan önemli bir kavramdır. Bu araştırmada da veri madenciliği yöntemlerinden olan karar ağaçları kullanılarak çalışan performansı verileri üzerinde sınıflandırma yapılmıştır. Çalışan performansı ölçeğindeki ifadelerden hangisinin veya hangilerinin performansın belirlenmesinde daha fazla etkili olduğu belirlenmiştir. Bu amaçla performans, kariyer planlama, iş tatmini ve kişilik arasındaki ilişkilerin incelenmesi için 2017 yılında yapılan bir anket çalışmasının verileri kullanılmıştır. Veriler üzerinde karar ağaçları yöntemi uygulanarak çalışanların performanslarının sınıflandırması işlemi gerçekleştirilmiştir. Çalışma sonucunda; karar ağaçlarının çalışan performansını yüksek başarı ile tahminlendiği görülmüştür. Ayrıca analiz sonucunda çalışan performansının belirlenmesinde ölçekteki hangi ifadelerin daha etkili olduğunu ortaya konmuştur.

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