Journal ArticleDOI
Automated daily pattern filtering of measured building performance data
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TLDR
In this paper, a day-typing process that uses Symbolic Aggregate approXimation (SAX), motif and discord extraction, and clustering to detect the underlying structure of building performance data is presented.About:
This article is published in Automation in Construction.The article was published on 2015-01-01. It has received 147 citations till now. The article focuses on the topics: Cluster analysis & Knowledge extraction.read more
Citations
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Journal ArticleDOI
Big Data in the construction industry
Muhammad Bilal,Lukumon O. Oyedele,Junaid Qadir,Kamran Munir,Saheed O. Ajayi,Olugbenga O. Akinade,Hakeem A. Owolabi,Hafiz Alaka,Maruf Pasha +8 more
TL;DR: This paper presents a wide-ranging interdisciplinary review of literature of fields such as statistics, data mining and warehousing, machine learning, and Big Data Analytics in the context of the construction industry and discusses the future potential of such technologies across the multiple domain-specific sub-areas of theConstruction industry.
Journal ArticleDOI
A review of fault detection and diagnostics methods for building systems
Woohyun Kim,Srinivas Katipamula +1 more
TL;DR: In this article, the authors provide a summary of automated fault detection and diagnostics studies published since 2004 that are relevant to the commercial buildings sector and provide a guideline for selecting an appropriate automated fault detector and diagnostic method.
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Big Data for Internet of Things: A Survey
TL;DR: This paper discusses the similarities and differences among Big Data technologies used in different IoT domains, suggests how certain Big Data technology used in one IoT domain can be re-used in another IoT domain, and develops a conceptual framework to outline the critical Big data technologies across all the reviewed IoT domains.
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Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data
TL;DR: This study investigates the potential of autoencoders in detecting anomalies in building energy data and proposes an autoencoder-based ensemble method that can be used as foundation for building professionals to develop advanced tools for anomaly detection and performance benchmarking.
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A review of smart building sensing system for better indoor environment control
TL;DR: A systemic review of how indoor sensors influence in managing optimal energy saving, thermal comfort, visual comfort, and indoor air quality in the built environment is provided.
References
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Some methods for classification and analysis of multivariate observations
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
Journal ArticleDOI
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
TL;DR: A new graphical display is proposed for partitioning techniques, where each cluster is represented by a so-called silhouette, which is based on the comparison of its tightness and separation, and provides an evaluation of clustering validity.
Proceedings ArticleDOI
The eyes have it: a task by data type taxonomy for information visualizations
TL;DR: A task by data type taxonomy with seven data types and seven tasks (overview, zoom, filter, details-on-demand, relate, history, and extracts) is offered.
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Clustering of time series data-a survey
TL;DR: This paper surveys and summarizes previous works that investigated the clustering of time series data in various application domains, including general-purpose clustering algorithms commonly used in time series clustering studies.
Proceedings ArticleDOI
Linear pattern matching algorithms
TL;DR: A linear time algorithm for obtaining a compacted version of a bi-tree associated with a given string is presented and indicated how to solve several pattern matching problems, including some from [4] in linear time.