The increasing reliance on Big Data platforms across various industries has necessitated the development of systematic decision-support frameworks to guide their evaluation and selection. Given the diversity of available platforms, each offering different capabilities, scalability, and computational efficiency, choosing the optimal solution remains a complex challenge. This research proposes a novel analytical framework that integrates Spherical Fuzzy Sets (SFS) with the Entropy and ORESTE methods to address uncertainty and enhance the accuracy and robustness of Big Data platform evaluation. This hybrid integration, not previously applied to Big Data platform selection, enables objective criteria weighting through the Entropy method and comprehensive alternative ranking using the ORESTE method. To validate the framework, seven real-world case studies were conducted across key industries in Egypt's urban towns, including health- care, energy, media, finance, telecommunications, supply chain, and retail. The results demonstrate that the proposed model effectively handles expert uncertainty and supports structured, data-driven decision- making. Comparative and sensitivity analyses further confirm the consistency of rankings and the reliability of the framework in complex multi-sector environments. This research provides a scalable, adaptable, and transparent decision-support tool for policymakers, industry leaders, and researchers aiming to optimize Big Data adoption and infrastructure in dynamic, data-intensive ecosystems.
Ali et al. (Sat,) studied this question.