Current Status and Future Opportunities for Big Data Research in the Construction Industry
Donghoon Lee1, Kyeong-Tae Jeong2, Boong Yeol Ryoo3

1Donghoon Lee, Department of Architectural Engineering, Hanbat National University, Daejeon, Korea, East Asian.

2Kyeong-Tae Jeong, Department of Architectural Engineering, Hanbat National University, Daejeon, Korea, East Asian.

3Boong Yeol Ryoo, Department of Consrtuction Science, Texas A&M University, Texas, U.S.A.

Manuscript received on 10 June 2019 | Revised Manuscript received on 17 June 2019 | Manuscript Published on 22 June 2019 | PP: 744-753 | Volume-8 Issue-8S2 June 2019 | Retrieval Number: H11240688S219/19©BEIESP

Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (

Abstract: Big data technology is expected that these technologies will bring new opportunities and values to various industries. However, the construction industry hasn’t put in much effort in using big data. This paper investigates past and current studies on big data analytics to identify the future technology for the construction industry. The research trend within the industry is examined for prediction and proposal of future opportunities. The study is conducted in stages as described below. First, this study investigates the implementation cases. After that, we determine the past and current research trends. Finally, this study analysis current trends and future directions of big data implementation. There is room for research in construction economics and market, business development, and portfolio management. Research activities in non-construction areas such as biology, management, and social science are the same level as the construction industry. As the proposed model for construction projects was generated based valid research activities, a big data analytics model can be used for implementing big data projects. This model is to divide construction data into project-specific (common) data and industry-specific (spatial) data. The model categorizes a variety of data contents based on characteristics of the data, either project specific data or industry-specific data. The data are then grouped by data analysis methods such as statistics, data mining, or machine learning methods. Finally, the data are grouped by the nature of data, either common information or regional information. The benefit of this approach is to assure the success of big data implementation projects by reconciling project information and industry information together since the success of a big data implementation relies on the integration of both data. Using this study, realistic resource planning and allocation for future can be projected in advance. Moreover, dynamic property of this model can provide to construction companies with real-time industry and project data.

Keywords: Big Data, Implementation Cases, Construction Industry, Big Data Analytics Model, Big Data Trend.
Scope of the Article: Big Data Analytics Application Systems