1 Ocak 2025 Dergi makalesi Açık Erişim

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

Citation Style Language JSON

{
  "DOI": "10.1002/spe.3391", 
  "abstract": "<p>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.</p>", 
  "author": [
    {
      "family": "Unlu", 
      "given": " Hueseyin"
    }, 
    {
      "family": "Yueruem", 
      "given": " Ozan Rasit"
    }, 
    {
      "family": "Yildiz", 
      "given": " Ali"
    }, 
    {
      "family": "Demirors", 
      "given": " Onur"
    }
  ], 
  "container_title": "SOFTWARE-PRACTICE & EXPERIENCE", 
  "id": "279147", 
  "issue": "3", 
  "issued": {
    "date-parts": [
      [
        2025, 
        1, 
        1
      ]
    ]
  }, 
  "page": "18", 
  "title": "Application of a Size Measurement Standard for Data Warehouse Projects", 
  "type": "article-journal", 
  "volume": "55"
}