Formalisms for query capture and data source identification to support data fusion for construction productivity monitoring

Abstract

Productivity monitoring, which involves frequent monitoring and analysis of on-going construction activities, helps in assessing a project's performance and in enabling identification of opportunities for improvement. It often involves finding answers for dynamic user queries that require data to be fused from different combinations of heterogeneous data sources having different levels of detail, representations and reference systems. Digital elements of these sources are expanding exponentially, and yet fusing and processing them manually remains a challenging problem. In this paper, the authors present a formal approach for capturing dynamic user queries and identifying applicable sets of data sources from a given set of available data sources to answer such queries. This approach is an important step to enable automated and efficient multi-source data fusion.