Enhancing Big Data Governance Framework Implementation Using Novel Fuzzy Frank Operators: An Application to MADM Process
محورهای موضوعی : Transactions on Fuzzy Sets and Systems
Hamza Iftikhar
1
(Department of Mathematics and Statistics, International Islamic University, Islamabad 44000, Pakistan. )
Faisal Mehmood
2
(School of Mathematics and Physics, Center for Applied Mathematics of Guangxi, Guangxi Minzu University, Nanning 530006, China.)
کلید واژه: Picture fuzzy set, Complex picture fuzzy set, Frank operations, Averaging operators, Geometric operators.,
چکیده مقاله :
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.
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.
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