Theoretical Architecture for Data-Quality-Aware Analytical Applications in the Construction Firms
Publication: Construction Research Congress 2022
ABSTRACT
The data-analytics solutions are currently more needed to be implemented regarding construction firms to allow for more flexible operations and higher productivity. Yet these data analytical applications are difficult to undertake because of low-level awareness and control of data quality. Through literature review, this paper identifies that the integrated applications that fuse multiple data sources and apply advanced algorithms become a research trend. However, an approach to assessing and controlling data quality is missing in these applications. This paper conceptualizes a novel framework for Data Quality Assessing and Controlling (DQAC), based on the cycle of Total Data Quality Management (TDQM). Then, this paper designs the architecture of DQAC and elaborates the functionalities and main components, which could be the theoretical foundation for building a data-quality-aware analytical application. The data quality measuring engine translates queries and retrieving quality information from the data wrapper, also it generates a data quality profile. Quality control strategies complete the TDQM cycle on both the level of extraction, transformation, and loading (ETL) process and data warehouse operation. Future research needs to develop a prototype to validate the DQAC using empirical data.
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Construction Research Congress 2022
Pages: 335 - 343
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Published online: Mar 7, 2022
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