Knowledge Discovery in Data: A Case Study

Ahmed Hammad, Simaan AbouRizk

Department of Civil and Environmental Engineering, Hole School of Construction Engineering and Management, University of Alberta, 3-014 Markin/CNRL Natural Resources Engineering Facility, Edmonton, Alberta, Canada.
HMD Project & Knowledge Management Services, Edmonton, Canada.

DOI: 10.4236/jcc.2014.25001                

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 useful knowledge for future projects. The model starts by understanding the problem domain, industrial construction projects. The second step is analyzing the problem data and its multiple dimensions. The target dataset is the labour resources data generated while managing industrial construction 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 dynamic 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 methodology. The proposed framework was applied to three different case studies to validate the applicability of the developed framework to real projects data.

Keywords

Construction Management; Project Management; Knowledge Management; Data Warehousing; Data Mining; Knowledge Discovery in Data (KDD); Industrial Construction; Labour Resources

Conflicts of Interest

The authors declare no conflicts of interest.

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