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It is common in industrial construction projects for data to becollected and discarded without being analyzed to extract useful knowledge. Aproposed integrated methodology based on a five-step Knowledge Discovery inData KDD model was developed to address this issue. The framework transfersexisting multidimensional historical data from completed projects into usefulknowledge for future projects. The model starts by understanding the problemdomain, industrial construction projects. The second step is analyzing theproblem data and its multiple dimensions. The target dataset is the labourresources data generated while managing industrial construction projects. Thenext step is developing the data collection model and prototype data ware-house. The data warehousestores collected data in a ready-for-mining format and produces dynamic On LineAnalytical Processing OLAP reports and graphs. Data was collected from alarge western-Canadian structural steel fabricator to prove the applicabilityof the developed methodology. The proposed framework was applied to threedifferent case studies to validate the applicability of the developed frameworkto real projects data.

KEYWORDS

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

Cite this paper

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.





Autor: Ahmed Hammad, Simaan AbouRizk

Fuente: http://www.scirp.org/



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