Expert’s evaluation for in the form of IVPFSN.

Correlation is an essential statistical concept for analyzing two dissimilar variables’ relationships. Although the correlation coefficient is a well-known indicator, it has not been applied to interval-valued Pythagorean fuzzy soft sets (IVPFSS) data. IVPFSS is a generalized form of interval-valued intuitionistic fuzzy soft sets and a refined extension of Pythagorean fuzzy soft sets. In this study, we propose the correlation coefficient (CC) and weighted correlation coefficient (WCC) for IVPFSS and examine their necessary properties. Based on the proposed correlation measures, we develop a prioritization technique for order preference by similarity to the ideal solution (TOPSIS). We use the Extract, Transform, and Load (ETL) software selection as an example to demonstrate the application of these measures and construct a prioritization technique for order preference by similarity to the ideal solution (TOPSIS) model. The method investigates the challenge of optimizing ETL software selection for business intelligence (BI). This study offers to illuminate the significance of using correlation measures to make decisions in uncertain and complex settings. The multi-attribute decision-making (MADM) approach is a powerful instrument with many applications. This expansion is predicted to conclude in a more reliable decision-making structure. Using a sensitivity analysis, we contributed empirical studies to determine the most significant decision processes. The proposed algorithm’s productivity is more consistent than prevalent models in controlling the adequate conformations of the anticipated study. Therefore, this research is expected to contribute significantly to statistics and decision-making.

相关性是分析两类异质变量间关联关系的核心统计学概念。尽管相关系数 (correlation coefficient, CC) 是学界公认的经典关联度量指标，但目前尚未有研究将其应用于区间值毕达哥拉斯模糊软集 (interval-valued Pythagorean fuzzy soft sets, IVPFSS) 数据中。IVPFSS是区间值直觉模糊软集的广义形式，同时也是毕达哥拉斯模糊软集的精细化扩展模型。本研究针对IVPFSS提出了相关系数与加权相关系数 (weighted correlation coefficient, WCC)，并系统论证了二者的必要性质。基于本文提出的关联度量方法，我们构建了逼近理想解排序法 (technique for order preference by similarity to the ideal solution, TOPSIS)。本文以抽取-转换-加载 (Extract, Transform, and Load, ETL) 软件选型为案例，演示了上述度量方法的应用流程，并搭建了逼近理想解排序法 (TOPSIS) 决策模型。该方法针对商业智能 (business intelligence, BI) 场景下的ETL软件选型优化难题展开了研究。本研究旨在阐明在不确定且复杂的决策环境中应用关联度量方法的重要价值。多属性决策 (multi-attribute decision-making, MADM) 方法是一类应用广泛的强有力决策工具，本文所提出的扩展框架有望构建出更为可靠的决策体系。通过敏感性分析，本文开展了实证研究以识别对决策流程影响最为显著的关键环节。相较于主流模型，本文所提出的算法在管控预期研究的合理构型方面表现出更强的一致性。因此，本研究有望为统计学与决策科学领域作出重要贡献。