1 Ocak 2025 Dergi makalesi Açık Erişim

Unlu, Hueseyin; Yueruem, Ozan Rasit; Yildiz, Ali; Demirors, Onur

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  <dc:creator>Unlu, Hueseyin</dc:creator>
  <dc:creator>Yueruem, Ozan Rasit</dc:creator>
  <dc:creator>Yildiz, Ali</dc:creator>
  <dc:creator>Demirors, Onur</dc:creator>
  <dc:date>2025-01-01</dc:date>
  <dc:description>MethodologyIn this research, we conducted a case study to establish a foundation for size measurement and effort estimation in DWH projects. We first applied a productivity-based estimation approach using linear regression with the ISBSG repository to assist organizations without historical data. We then evaluated various machine learning algorithms to improve estimation accuracy. Finally, we tested a combined model that integrates both approaches for estimating effort in external projects.ResultsUsing the ISBSG dataset, linear regression models based on productivity achieved a Mean Magnitude of Relative Error (MMRE) of 0.285. Machine learning algorithms improved accuracy by 22.81%, reducing the MMRE to 0.220. The final model, applied to external projects, yielded MRE values between 0.010 and 0.245.ConclusionThe ISBSG repository is a valuable resource for effort estimation in DWH projects. Combining productivity-based estimation with machine learning enhances accuracy and predictive performance, making it a more reliable approach than traditional models.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/279147</dc:identifier>
  <dc:identifier>oai:aperta.ulakbim.gov.tr:279147</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:source>SOFTWARE-PRACTICE &amp; EXPERIENCE 55(3) 18</dc:source>
  <dc:title>Application of a Size Measurement Standard for Data Warehouse Projects</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
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