Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Barriers of Automated BIM Use: Examining Factors of Project Delivery Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

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

verfasst von : Ekaterina Petrova, Pieter Pauwels, Kjeld Svidt, Rasmus Lund Jensen

Erschienen in: Advances in Informatics and Computing in Civil and Construction Engineering

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Simulation of Construction Processes as a Link Between BIM Models and Construction Progression On-site
Nächstes Kapitel The Role of Knowledge-Based Information on BIM for Built Heritage
Fußnoten
Literatur
1.
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17(3), 37–54 (1996)
2.
Soibelman, L., Kim, H.: Data preparation process for construction knowledge generation through knowledge discovery in databases. J. Comput. Civil Eng. 16(1), 39–48 (2002)CrossRef
3.
Hand, D., Mannila, H., Smyth, P.: Principles of Data Mining. MIT Press, Cambridge (2001)
4.
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, NY (2006)MATH
5.
Piatetsky-Shapiro, G.: Knowledge discovery in real databases: a report on the IJCAI-89 workshop. AI Mag. 11(5), 68–70 (1991)
6.
Han, J.W., Kamber, M., Pei, J.: Data Mining Concepts and Techniques, 3rd edn. Morgan Kaufmann, Waltham, US (2012)MATH
7.
Fan, C., Xiao, F., Li, Z., Wang, J.: Unsupervised data analytics in mining big building operational data for energy efficiency enhancement: a review. Energy Build. 159, 296–308 (2018)CrossRef
8.
Ahmed, A., Korres, N.E., Ploennigs, J., Elhadi, H., Menzel, K.: Mining building performance data for energy-efficient operation. Adv. Eng. Inform. 25, 341–354 (2011)CrossRef
9.
Wang, Z., Srinivasan, R.S.: A review of artificial intelligence based building energy use prediction: contrasting the capabilities of single and ensemble prediction models. Renew. Sustain. Energy Rev. 75, 796–808 (2017)CrossRef
10.
Zhao, H., Magoulès, F.: A review on the prediction of building energy consumption. Renew. Sustain. Energy Rev. 16, 3586–3592 (2012)CrossRef
11.
D’Oca, S., Hong, T.: A data-mining approach to discover patterns of window opening and closing behavior in offices. Build. Environ. 82, 726–739 (2014)CrossRef
12.
Zhao, J., Lasternas, B., Lam, K.P., Yun, R., Loftness, V.: Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining. Energy Build. 82, 341–355 (2014)CrossRef
13.
Cheng, Z., Zhao, Q., Wang, F., Chen, Z., Jiang, Y., Li, Y.: Case studies of fault diagnosis and energy saving in buildings using data mining techniques. In: IEEE International Conference on Automation Science and Engineering, pp. 646–651 (2016)
14.
Pena, M., Biscarri, F., Guerrero, J.I., Monedero, I., León, C.: Rule-based system to detect energy efficiency anomalies in smart buildings, a data mining approach. Expert Syst. Appl. 56, 242–255 (2016)CrossRef
15.
D’Oca, S., Hong, T.: Occupancy schedules learning process through a data mining framework. Energy Build. 88, 395–408 (2015)CrossRef
16.
Fan, C., Xiao, F., Yan, C.: A framework for knowledge discovery in massive building automation data and its application in building diagnostics. Autom. Constr. 50, 81–90 (2015)CrossRef
17.
Yu, Z., Fung, B., Haghighat, F.: Extracting knowledge from building-related data—a data mining framework. Build. Simul. 6(2), 207–222 (2013)CrossRef
18.
Xiao, F., Fan, C.: Data mining in building automation system for improving building operational performance. Energy Build. 75, 109–118 (2014)CrossRef
19.
Miller, C., Nagy, Z., Schlueter, A.: Automated daily pattern filtering of measured building performance data. Autom. Constr. 49, 1–17 (2015)CrossRef
20.
Wu, S., Clements-Croome, D.: Understanding the indoor environment through mining sensory data—a case study. Energy Build. 39, 1183–1191 (2007)CrossRef
21.
Jun, M.A., Cheng, J.C.P.: Selection of target LEED credits based on project information and climatic factors using data mining techniques. Adv. Eng. Inform. 32, 224–236 (2017)CrossRef
22.
Peng, Y., Lina, J.R., Zhang, J.P., Hu, Z.Z.: A hybrid data mining approach on BIM-based building operation and maintenance. Build. Environ. 126, 483–495 (2017)CrossRef
23.
Yarmohammadi, S., Pourabolghasem, R., Shirazi, A., Ashuri, B.: A sequential pattern mining approach to extract information from BIM design log files. In: 33rd International Symposium on Automation and Robotics in Construction, pp. 174–181 (2016)
24.
Liu, Y., Huang, Y.C., Stouffs, R.: Using a data-driven approach to support the design of energy-efficient buildings. ITCon 20, 80–96 (2015)
25.
Mirakhorli, M., Chen, H., Kazman, R.: Mining big data for detecting, extracting and recommending architectural design concepts. In: IEEE/ACM 1st International Workshop on Big Data Software Engineering, pp. 15–18 (2015)
26.
Rasmussen, M.H., Pauwels, P., Hviid, C.A., Karlshøj, J.: Proposing a central AEC ontology that allows for domain specific extensions. In: Proceedings of the Joint Conference on Computing in Construction (JC3), pp. 237–244 (2017)
27.
Pauwels, P., Zhang, S., Lee, Y.C.: Semantic web technologies in AEC industry: a literature overview. Autom. Constr. 73, 145–165 (2017)CrossRef
28.
Ristoski, P., Paulheim, H.: Semantic web in data mining and knowledge discovery: a comprehensive survey. J. Web Semant. 36, 1–22 (2016)CrossRef
29.
Rodriguez, I., Lauridsen, M., Vasluianu, G., Poulsen, A.N., Mogensen, P.: The Gigantium smart city living lab: a multi-arena LoRa-based testbed. In: 15th International Symposium on Wireless Communication Systems, Lisbon, Portugal (2018) (in press)
30.
Fan, C., Xiao, F., Madsen, H., Wang, D.: Temporal knowledge discovery in big BAS data for building energy management. Energy Build. 109, 75–89 (2015)CrossRef
31.
Fu, T.C.: A review on time series data mining. Eng. Appl. Artif. Intell. 17, 164–181 (2011)CrossRef
32.
Patel, P., Keogh, E., Lin, J., Lonardi, S.: Mining motifs in massive time series databases. In: Proceedings of the 2002 IEEE International Conference on Data Mining. (2002)
33.
Weiner, P.: Linear pattern matching algorithms. In: 14th Annual IEEE Symposium on Switching and Automata Theory, pp. 1–11 (1973)
34.
Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Disc. 8 (2004)MathSciNetCrossRef
Metadaten
Titel
In Search of Sustainable Design Patterns: Combining Data Mining and Semantic Data Modelling on Disparate Building Data
verfasst von
Ekaterina Petrova
Pieter Pauwels
Kjeld Svidt
Rasmus Lund Jensen
Copyright-Jahr
2019
Buch
Advances in Informatics and Computing in Civil and Construction Engineering

Print ISBN: 978-3-030-00219-0

Electronic ISBN: 978-3-030-00220-6

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-00220-6_3