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Application of a Size Measurement Standard for Data Warehouse Projects.

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Bibliographic Details
Title: Application of a Size Measurement Standard for Data Warehouse Projects.
Authors: Ünlü, Hüseyin, Yürüm, Ozan Raşit, Yıldız, Ali, Demirörs, Onur
Source: Software: Practice & Experience; Mar2025, Vol. 55 Issue 3, p571-588, 18p
Subject Terms: MACHINE learning, MEASUREMENT, ESTIMATION theory, REGRESSION analysis, PROJECT management, DATA management
Abstract: Methodology: In 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. Results: Using 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. Conclusion: The 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. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:Methodology: In 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. Results: Using 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. Conclusion: The 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. [ABSTRACT FROM AUTHOR]
ISSN:00380644
DOI:10.1002/spe.3391