1 Ocak 2025 Dergi makalesi Açık Erişim

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

JSON

{
  "conceptrecid": "279146", 
  "created": "2025-04-17T19:45:11.279422+00:00", 
  "doi": "10.1002/spe.3391", 
  "files": [
    {
      "bucket": "52021538-e4ba-47df-b08f-c08fd5df36ae", 
      "checksum": "md5:22e8ec020dcfa1bd823f025804d85760", 
      "key": "bib-3129b6f5-943e-44e3-80e7-cc5dc14a79fd.txt", 
      "links": {
        "self": "https://aperta.ulakbim.gov.tr/api/files/52021538-e4ba-47df-b08f-c08fd5df36ae/bib-3129b6f5-943e-44e3-80e7-cc5dc14a79fd.txt"
      }, 
      "size": 179, 
      "type": "txt"
    }
  ], 
  "id": 279147, 
  "links": {
    "badge": "https://aperta.ulakbim.gov.tr/badge/doi/10.1002/spe.3391.svg", 
    "bucket": "https://aperta.ulakbim.gov.tr/api/files/52021538-e4ba-47df-b08f-c08fd5df36ae", 
    "doi": "https://doi.org/10.1002/spe.3391", 
    "html": "https://aperta.ulakbim.gov.tr/record/279147", 
    "latest": "https://aperta.ulakbim.gov.tr/api/records/279147", 
    "latest_html": "https://aperta.ulakbim.gov.tr/record/279147"
  }, 
  "metadata": {
    "access_right": "open", 
    "access_right_category": "success", 
    "communities": [
      {
        "id": "tubitak-destekli-proje-yayinlari"
      }
    ], 
    "creators": [
      {
        "affiliation": "Izmir Inst Technol, Izmir, Turkiye", 
        "name": "Unlu, Hueseyin"
      }, 
      {
        "affiliation": "Izmir Inst Technol, Izmir, Turkiye", 
        "name": "Yueruem, Ozan Rasit"
      }, 
      {
        "name": "Yildiz, Ali"
      }, 
      {
        "affiliation": "Izmir Inst Technol, Izmir, Turkiye", 
        "name": "Demirors, Onur"
      }
    ], 
    "description": "<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>", 
    "doi": "10.1002/spe.3391", 
    "has_grant": false, 
    "journal": {
      "issue": "3", 
      "pages": "18", 
      "title": "SOFTWARE-PRACTICE & EXPERIENCE", 
      "volume": "55"
    }, 
    "license": {
      "id": "cc-by"
    }, 
    "publication_date": "2025-01-01", 
    "relations": {
      "version": [
        {
          "count": 1, 
          "index": 0, 
          "is_last": true, 
          "last_child": {
            "pid_type": "recid", 
            "pid_value": "279147"
          }, 
          "parent": {
            "pid_type": "recid", 
            "pid_value": "279146"
          }
        }
      ]
    }, 
    "resource_type": {
      "subtype": "article", 
      "title": "Dergi makalesi", 
      "type": "publication"
    }, 
    "science_branches": [
      "Di\u011fer"
    ], 
    "title": "Application of a Size Measurement Standard for Data Warehouse Projects"
  }, 
  "owners": [
    1
  ], 
  "revision": 1, 
  "stats": {
    "downloads": 0.0, 
    "unique_downloads": 0.0, 
    "unique_views": 0.0, 
    "version_downloads": 0.0, 
    "version_unique_downloads": 0.0, 
    "version_unique_views": 0.0, 
    "version_views": 0.0, 
    "version_volume": 0.0, 
    "views": 0.0, 
    "volume": 0.0
  }, 
  "updated": "2025-04-17T19:45:11.318518+00:00"
}