Knowledge Discovery in Data: A Case Study

DOI: 10.4236/jcc.2014.25001   PDF   HTML   XML   3,924 Downloads   6,384 Views   Citations


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.

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Hammad, A. and AbouRizk, S. (2014) Knowledge Discovery in Data: A Case Study. Journal of Computer and Communications, 2, 1-28. doi: 10.4236/jcc.2014.25001.

Conflicts of Interest

The authors declare no conflicts of interest.


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