Automating Common Data Integration for Improved Data-Driven Decision-Support System in Industrial Construction

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.