1 Ocak 2025 Dergi makalesi Açık Erişim

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

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/279147</identifier>
  <creators>
    <creator>
      <creatorName>Unlu, Hueseyin</creatorName>
      <givenName>Hueseyin</givenName>
      <familyName>Unlu</familyName>
      <affiliation>Izmir Inst Technol, Izmir, Turkiye</affiliation>
    </creator>
    <creator>
      <creatorName>Yueruem, Ozan Rasit</creatorName>
      <givenName>Ozan Rasit</givenName>
      <familyName>Yueruem</familyName>
      <affiliation>Izmir Inst Technol, Izmir, Turkiye</affiliation>
    </creator>
    <creator>
      <creatorName>Yildiz, Ali</creatorName>
      <givenName>Ali</givenName>
      <familyName>Yildiz</familyName>
    </creator>
    <creator>
      <creatorName>Demirors, Onur</creatorName>
      <givenName>Onur</givenName>
      <familyName>Demirors</familyName>
      <affiliation>Izmir Inst Technol, Izmir, Turkiye</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Application Of A Size Measurement Standard For Data Warehouse Projects</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2025</publicationYear>
  <dates>
    <date dateType="Issued">2025-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/279147</alternateIdentifier>
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  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1002/spe.3391</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;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.&lt;/p&gt;</description>
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