Abstract
Big data have become an integral part of various research fields due to the rapid advancements in the digital technologies available for dealing with data. The construction industry is no exception and has seen a spike in the data being generated due to the introduction of various digital disruptive technologies. However, despite the availability of data and the introduction of such technologies, the construction industry is lagging in harnessing big data. This paper critically explores literature published since 2010 to identify the data trends and how the construction industry can benefit from big data. The presence of tools such as computer-aided drawing (CAD) and building information modelling (BIM) provide a great opportunity for researchers in the construction industry to further improve how infrastructure can be developed, monitored, or improved in the future. The gaps in the existing research data have been explored and a detailed analysis was carried out to identify the different ways in which big data analysis and storage work in relevance to the construction industry. Big data engineering (BDE) and statistics are among the most crucial steps for integrating big data technology in construction. The results of this study suggest that while the existing research studies have set the stage for improving big data research, the integration of the associated digital technologies into the construction industry is not very clear. Among the future opportunities, big data research into construction safety, site management, heritage conservation, and project waste minimization and quality improvements are key areas.
Highlights
IntroductionThe rapidity with which data is generated and piled up in the era of disruptive digital technologies is astounding [1]
Big data are increasingly becoming an integral part of almost all fields
This paper focuses on horizontal scaling platforms (HSP), Berkeley Data Analytics Stack (BDAS) and Hadoop
Summary
The rapidity with which data is generated and piled up in the era of disruptive digital technologies is astounding [1]. Such big data have necessitated the need for efficient data management tools and techniques to deal with the bulk of data. A great deal of focus has been dedicated to using, storing, and managing big data in various fields [2]. The bulk of data generated daily through these technologies has made various researchers interested in using the data for innovative purposes and moving away from traditional time-consuming questionnaire-based approaches for data collection to more digital data management. The presence of bulks of data makes it possible for researchers to make informed decisions and conduct relevant analyses for their field of study
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