A cloud-based system framework for performing online viewing, storage, and analysis on big data of massive BIMs

作者：Chen Hung Ming^*; Chang Kai Chuan; Lin Tsung Hsi

来源：Automation in Construction, 2016, 71: 34-48.

DOI：10.1016/j.autcon.2016.03.002

• Cloud computing
• Building information model
• Big data analysis
• Web3D
• Bigtable
• MapReduce

This paper presents a cloud-based system framework based on Bigtable and MapReduce as the data storage and processing paradigms for providing a web-based service for viewing, storing, and analyzing massive building information models (BIMs). Cloud and Web 3D technologies were utilized to develop a BIM data center that can handle the big data of massive BlMs using multiple servers in a distributed manner and can be accessed by multiple users to concurrently submit and view BIMs online in 3D. Traditional BIM include only static information such as the geometric parameters, physical properties, and spatial relations for modeling a physical space. In this study, BIM was extended to dynamic BIM, which includes dynamic data such as historical records from the monitoring of the facility environment and usage. Owing to this extension, a dynamic BIM became a parametric model, which can be used to simulate user behaviors. On the client side, this study applied WebGL in the web interface development to achieve the display of BIMs in 3D on browsers. Users can access the services via various online devices anytime and anywhere to view the 3D model online. On the server side, this study used Apache Hadoop, which can utilize multiple servers to provide mass storage spaces in a distributed manner with Bigtable-like structured storage, to establish the BIM data center. A schema for storing the big data of massive dynamic BIMs in Bigtables was proposed. MapReduce, a Hadoop component for the parallel processing of large data sets, was utilized to process big data from dynamic BIMs. A big data analysis framework to effectively retrieve and calculate required information from dynamic BIMs in the data center for various applications by MapReduce distributed computing was proposed this study. We provide principle and architecture of the proposed framework along with its experimental assessment. The results confirmed that scalable and reliable management of massive BIMs can be achieved using the proposed framework.

• 出版日期2016-11