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Enabling building digital twin: Ontology-based information management framework for multi-source data integration

Author(s):





Medium: journal article
Language(s): English
Published in: IOP Conference Series: Earth and Environmental Science, , n. 9, v. 1101
Page(s): 092010
DOI: 10.1088/1755-1315/1101/9/092010
Abstract:

The emergence of the digital twin concept can potentially change the way people manage built assets thoroughly. This is because the semantics-based model and linked data approach behind the digital twin, as the successor of classical BIM, provide strong capability in integrating data from fragmented and heterogeneous sources and thus enable better-informed decision-making. Taking buildings as the case, this paper demonstrates the ontology-based Information Management Framework and elaborates on the process to integrate data through a common data model. Specifically, the Foundation Data Model (FDM) representing the operation of buildings and embedded systems is developed and two patterns of integration architecture are compared. To conceptualise all the essential entities and relationships, the building topology ontology and BRICK ontology are reused and merged to serve as a feasible FDM. According to the characteristic of asset management services that digital twins support, two integration architectures are compared, including the data warehouse approach and the mediator approach. A case study is presented to elaborate on the implementation of these two approaches and their applicability. This work sets out the standardised and modularised paradigms for discovering, fetching, and integrating data from disparate sources with different data curation manners.

License:

This creative work has been published under the Creative Commons Attribution 3.0 Unported (CC-BY 3.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10780546
  • Published on:
    12/05/2024
  • Last updated on:
    12/05/2024
 
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