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A Framework for Data Lifecycle Model Selection

Author

Listed:
  • Mauro Iacono

    (Dipartimento di Matematica e Fisica, Università degli Studi della Campania “L. Vanvitelli”, 81100 Caserta, Italy
    These authors contributed equally to this work.)

  • Michele Mastroianni

    (Dipartimento di Scienze Agrarie, Alimenti, Risorse Naturali e Ingegneria, Università degli Studi di Foggia, 71122 Foggia, Italy
    These authors contributed equally to this work.)

  • Christian Riccio

    (Dipartimento di Matematica e Fisica, Università degli Studi della Campania “L. Vanvitelli”, 81100 Caserta, Italy
    These authors contributed equally to this work.)

  • Bruna Viscardi

    (Independent Researcher, 81100 Caserta, Italy
    These authors contributed equally to this work.)

Abstract

The selection of Data Lifecycle Models (DLMs) in complex data management scenarios necessitates finding a balance between quantitative and qualitative characteristics to ensure regulation, improve performance, and maintain governance requirements. In this context, an interactive web application based on AHP-Express has been developed as a user-friendly tool to facilitate decision-making processes related to DLM. The application facilitates customized decision matrices, organizes various expert interviews with distinct weights, calculates local and global priorities, and delivers final DLM rankings by consolidating sub-criteria scores into weighted macro-category values, accompanied by graphical representations. Key functions encompass consistency checks, sensitivity analysis for macro-category weight variations, and graphical representations (bar charts, radar maps, sensitivity charts) that emphasize strengths, shortcomings, and the robustness of rankings. In a suggested application for sensor-based artifact monitoring at the Museo del Carbone, the tool swiftly selected the most appropriate DLM as the leading contender, exhibiting consistent performance across diverse weight scenarios. The results of the Museo del Carbone case validate that AHP-Express facilitates rapid, transparent, and reproducible DLM selection, reducing cognitive load while maintaining scientific rigor. The tool’s modular architecture and visualization features enable educated decision making for various data management issues.

Suggested Citation

  • Mauro Iacono & Michele Mastroianni & Christian Riccio & Bruna Viscardi, 2025. "A Framework for Data Lifecycle Model Selection," Future Internet, MDPI, vol. 17(9), pages 1-21, August.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:9:p:390-:d:1736691
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    References listed on IDEAS

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