فهرس المقالات ‎Frank operations‎

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  1 - ‎Enhancing Big Data Governance Framework Implementation Using Novel Fuzzy Frank Operators‎: ‎An Application to MADM Process
  Hamza Iftikhar Faisal  Mehmood
  10.30495/TFSS.2024.1105557
  In today's data-driven landscape‎, ‎to ensure continuous survival and betterment‎, ‎the implementation of a robust Big Data Governance Framework (BDGF) is imperative for organizations to effectively manage and harness the potential of their vast data resources‎. ‎The BD أکثر

  In today's data-driven landscape‎, ‎to ensure continuous survival and betterment‎, ‎the implementation of a robust Big Data Governance Framework (BDGF) is imperative for organizations to effectively manage and harness the potential of their vast data resources‎. ‎The BDGF serves no purpose when implemented in a random manner‎. ‎This article delves into the complex decision-making challenges that emerge in the context of implementation of the BDGF under uncertain conditions‎. ‎Specifically‎, ‎we aim to analyze and evaluate the BDGF performance using the Multi-Attribute Decision-Making (MADM) techniques aiming to address the intricacies of big data governance uncertainties‎. ‎To achieve our objectives‎, ‎we explore the application of Frank operators within the framework of complex picture fuzzy (CPF) sets (CPFs)‎. ‎We introduce complex picture fuzzy Frank weighted averaging (CPFFWA) and complex picture fuzzy Frank ordered weighted averaging (CPFFOWA) operators to enable more accurate implementation of the BDGF‎. ‎Additionally‎, ‎we rigorously examine the reliability of these newly proposed fuzzy Frank (FF) operators (FFAOs)‎, ‎taking into consideration essential properties such as idempotency‎, ‎monotonicity‎, ‎and boundedness‎. ‎To illustrate the practical applicability of our approach‎, ‎we present a case study that highlights the decision-making challenges encountered in the implementation of the BDGF‎. ‎Subsequently‎, ‎we conduct a comprehensive numerical example to assess various BDGF implementation options using the MADM technique based on complex picture fuzzy Frank aggregation (CPFFA) operators‎. ‎Furthermore‎, ‎we perform a comprehensive comparative assessment of our proposed methodology‎, ‎emphasizing the significance of the novel insights and results derived‎. ‎In conclusion‎, ‎this research article offers a unique and innovative perspective on decision-making within the realm of the BDGF‎. ‎By employing the CPFFWA and the CPFFOWA operators‎, ‎organizations can make well-informed decisions to optimize their BDGF implementations‎, ‎mitigate uncertainties‎, ‎and harness the full potential of their data assets in an ever-evolving data landscape‎. ‎This work contributes to the advancement of decision support systems for big data governance (BDG)‎, ‎providing valuable insights for practitioners and scholars alike‎. تفاصيل المقالة