Knowledge Discovery in Data: A Case Study

Abstract

It is common in industrial construction projects for data to be collected and discarded without being analyzed to extract useful knowledge. A proposed integrated methodology based on a five-step Knowledge Discovery in Data (KDD) model was developed to address this issue. The framework transfers existing multidimensional historical data from completed projects into use-ful knowledge for future projects. The model starts by understanding the problem domain, indus-trial construction projects. The second step is analyzing the problem data and its multiple dimen-sions. The target dataset is the labour resources data generated while managing industrial con-struction projects. The next step is developing the data collection model and prototype data ware-house. The data warehouse stores collected data in a ready-for-mining format and produces dy-namic On Line Analytical Processing (OLAP) reports and graphs. Data was collected from a large western-Canadian structural steel fabricator to prove the applicability of the developed metho-dology. The proposed framework was applied to three different case studies to validate the appli-cability of the developed framework to real projects data.