In Search of Sustainable Design Patterns: Combining Data Mining and Semantic Data Modelling on Disparate Building Data

  • Ekaterina PetrovaEmail author
  • Pieter Pauwels
  • Kjeld Svidt
  • Rasmus Lund Jensen
Conference paper


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.


BIM Semantics Data mining Pattern recognition Knowledge discovery 



The authors would like to thank Dr. Mads Lauridsen and Aalborg Municipality for providing access to the sensor data used to perform the experiment.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Civil EngineeringAalborg UniversityAalborgDenmark
  2. 2.Department of Architecture and Urban PlanningGhent UniversityGhentBelgium

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