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In search of sustainable design patterns : combining data mining and semantic data modelling on disparate building data

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Abstract
Cross-domain analytical techniques have made the prediction of outcomes in building design more accurate. Yet, many decisions are based on rules of thumb and previous experiences, and not on documented evidence. That results in inaccurate predictions and a difference between predicted and actual building performance. This article aims to reduce the occurrence of such errors using a combination of data mining and semantic modelling techniques, by deploying these technologies in a use case, for which sensor data is collected. The results present a semantic building data graph enriched with discovered motifs and association rules in observed properties. We conclude that the combination of semantic modelling and data mining techniques can contribute to creating a repository of building data for design decision support.
Keywords
BIM, semantics, data mining, pattern recognition, knowledge discovery

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Chicago
Petrova, Ekaterina, Pieter Pauwels, Kjeld Svidt, and Rasmus Lund Jensen. 2019. “In Search of Sustainable Design Patterns : Combining Data Mining and Semantic Data Modelling on Disparate Building Data.” In Advances in Informatics and Computing in Civil and Construction Engineering, ed. Ivan Mutis and Timo Hartmann, 19–26. Switzerland: Springer .
APA
Petrova, E., Pauwels, P., Svidt, K., & Jensen, R. L. (2019). In search of sustainable design patterns : combining data mining and semantic data modelling on disparate building data. In I. Mutis & T. Hartmann (Eds.), Advances in informatics and computing in civil and construction engineering (pp. 19–26). Presented at the 35th CIB W78 2018 Conference, Switzerland: Springer .
Vancouver
1.
Petrova E, Pauwels P, Svidt K, Jensen RL. In search of sustainable design patterns : combining data mining and semantic data modelling on disparate building data. In: Mutis I, Hartmann T, editors. Advances in informatics and computing in civil and construction engineering. Switzerland: Springer ; 2019. p. 19–26.
MLA
Petrova, Ekaterina et al. “In Search of Sustainable Design Patterns : Combining Data Mining and Semantic Data Modelling on Disparate Building Data.” Advances in Informatics and Computing in Civil and Construction Engineering. Ed. Ivan Mutis & Timo Hartmann. Switzerland: Springer , 2019. 19–26. Print.
@inproceedings{8576024,
  abstract     = {Cross-domain analytical techniques have made the prediction of outcomes in building design more accurate. Yet, many decisions are based on rules of thumb and previous experiences, and not on documented evidence. That results in inaccurate predictions and a difference between predicted and actual building performance. This article aims to reduce the occurrence of such errors using a combination of data mining and semantic modelling techniques, by deploying these technologies in a use case, for which sensor data is collected. The results present a semantic building data graph enriched with discovered motifs and association rules in observed properties. We conclude that the combination of semantic modelling and data mining techniques can contribute to creating a repository of building data for design decision support.},
  author       = {Petrova, Ekaterina and Pauwels, Pieter and Svidt, Kjeld and Jensen, Rasmus Lund},
  booktitle    = {Advances in informatics and computing in civil and construction engineering},
  editor       = {Mutis, Ivan and Hartmann, Timo},
  isbn         = {9783030002190},
  language     = {eng},
  location     = {Chicago, IL, US},
  pages        = {19--26},
  publisher    = {Springer },
  title        = {In search of sustainable design patterns : combining data mining and semantic data modelling on disparate building data},
  url          = {http://dx.doi.org/10.1007/978-3-030-00220-6\_3},
  year         = {2019},
}

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