Automating Common Data Integration for Improved Data-Driven Decision-Support System in Industrial Construction
Publication: Journal of Computing in Civil Engineering
Volume 36, Issue 2
Abstract
To achieve meaningful results, data-driven decision-support systems in construction require the integration of fragmented data from multiple standalone databases. In practice, a manual brute-force approach is often the only available means of integrating structured, yet semantically-ambiguous, construction data. Two common data integration challenges include the identification of (1) key strings (i.e., product identification) partially shared between two data sources; and (2) relationships (overlap, included, or outside) between two 3D object lists based on coordinates. This research has developed a framework that includes two generic solutions to the identified semantic mapping challenges. The proposed framework automatically integrates fragmented and incompatible data (exhibiting similar semantic mapping challenges) from various sources into a tidy format for input into a diverse range of industrial construction applications. Verification and functionality of the framework were confirmed using both artificial data and a real case study of a large oil-and-gas project. The ability of the proposed data integration functions and framework to automate otherwise manual data integration processes was demonstrated. Results of this study are expected to enhance real-time information flow, improve data quality, and promote the use of fragmented data for critical decision support in practice.
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Data Availability Statement
Case study data were provided by a third party; requests for this material should be directed to the provider indicated in the Acknowledgments. Code for the LCStr and detect3Dr custom functions are available from https://doi.org/10.5281/zenodo.4057067 or https://doi.org/10.5281/zenodo.4058576, respectively.
Acknowledgments
This project was supported by a Collaborative Research and Development Grant (CRDPJ 492657) from the Natural Sciences and Engineering Council of Canada. The authors would like to thank PCL Industrial Management Inc. for their continued support and for providing case study data.
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Information
Published In
Journal of Computing in Civil Engineering
Volume 36 • Issue 2 • March 2022
Copyright
© 2021 American Society of Civil Engineers.
History
Received: May 21, 2021
Accepted: Sep 22, 2021
Published online: Dec 14, 2021
Published in print: Mar 1, 2022
Discussion open until: May 14, 2022
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