One of the most primary issues that organizations have to deal with is incorporating massive structured data problems, simultaneously. Additionally, a vital division in any organization is the department of human resources (HR), which is in charge of the recruitment and personnel selection procedures. Due to the nature of the personnel assessment problems, which include multiple candidates as alternatives along with various complex evaluating criteria, these types of problems can be tackled by the aid of multi-attribute decision making (MADM) techniques. Moreover, in mega-structured organizations, the procedure of personnel selection contains massive structures of data due to the number of potential candidates for job positions in various sub-divisions and departments. Therefore, the personnel selection problem in such environments can be subjected as a big data problem which should be handled prudently to save time and cost. The main objective of the current study is to extend the CLUS-MCDA approach (CLUSter analysis for improving Multiple Criteria Decision Analysis) and integrate it with the Best–Worst Method (BWM) and a specific structure to solve multi-scenario big data decision-making problems. In this study, to validate the practicality and reliability of the W-CLUS-MCDA approach, multiple personnel selection and risk assessment problems have been investigated with various scenarios within several departments, simultaneously. This study has also introduced the concept of multi-scenario parallel decision making (PDM) within the context of MADM methodology using a data-driven decision-making approach solving various big data problems.

组织必须处理的最主要问题之一是同时合并大量结构化数据问题。此外，在任何组织中，至关重要的部门是人力资源部（HR），该部门负责招聘和人员选拔程序。由于人员评估问题的性质，其中包括多个候选人作为备选方案以及各种复杂的评估标准，因此可以借助多属性决策（MADM）技术来解决这些类型的问题。此外，在大型组织中，人员选拔程序包含大量数据结构，这是由于各个子部门和部门的潜在职位候选人数量众多。因此，在这种环境下的人员选拔问题可能会成为大数据问题，应谨慎处理以节省时间和成本。当前研究的主要目标是扩展CLUS-MCDA方法（CLUSter分析以改善多标准决策分析），并将其与最佳-最差方法（BWM）和特定结构集成以解决多场景大数据决策。制造问题。在这项研究中，为验证W-CLUS-MCDA方法的实用性和可靠性，已在多个部门的不同情况下同时调查了多人选拔和风险评估问题。