## 5 Conclusions  
In today's fast-changing environment, there is a clear worldwide shift towards using large-scale datasets by organizations utilizing DSSs. Instead of building their input datasets from scratch, these organizations are more frequently choosing to acquire and effortlessly incorporate large-scale datasets in their current systems. These datasets are essential for supporting a broad range of complex operational processes. In light of this, the development of meticulously structured and robust data collections is crucial for assisting in the management of complex missions and supporting various operational processes. These processes encompass, among others, design, simulation, monitoring, and control. Complying with these complex requirements requires cooperative efforts among different departments within organizations, bringing together their expertise to address the detailed needs of mission support. However, the complexities associated with these large-scale datasets cannot be underestimated. They display increasing levels of complexity, diversity, and variability, which continue to grow rapidly. This rapid expansion not only limits the variety of missions that traditional SSs can effectively handle but also reduces their overall effectiveness. For instance, issues include difficulties in updating and upgrading the system, limitations in flexible DSS expansion, decreasing IS efficiency, modification of any type of SS, the capability of DSS upgrading, etc. Consequently, to surmount the important challenges presented by the complex, expanding, and diverse nature of large-scale datasets, this study introduces an extensive array of methods and techniques for the development and maintenance of large-scale DSSs. At the core of it, the solution proposed in the present study is the design of a CMS sub-system which is a part of the overall DSS framework. This system is specifically tailored for effectively designing, monitoring, and controlling operational procedures. It is characterized by its high organization, adaptability, and viability, as well as its low costs in design, maintenance, and upgrading. Indeed, the system has multiple strengths: It is multi-thematic, capable of handling a wide range of themes without becoming overloaded. It facilitates the management of highly complex datasets and its associated data cores, and it is scalable to very large sizes while remaining manageable. Thematic changes can be easily implemented by systematically organizing the data core of the system. Moreover, any modifications to the database, thedata core, the computational methodologies of the system, and the associated external data sources are dynamic and can be made at any stage in the proposed system's lifecycle. Building on this, the proposed solution of the present study is a systematic and orthological way to construct suitable organized, general, common, and adaptable data cores in order to support the operational procedures for a significant number of different missions. Our proposed innovative framework is conceptualized to aid engineers in developing CMSSs that are highly user-friendly and applicable in operational scenarios. This is achieved thanks to human-reasoning-centered design. The transition from just a theoretical framework to an implementable solution for simple but realistic complex missions is currently undergoing validation. This is ensured through ongoing Proofs of Concept (PoCs), which are in the final stages of their implementation. Consideration for publication of this work is scheduled for the near future. Nonetheless, the applicability of these methods was validated in this work (see Appendices C and D and Supplementary Materials). This holistic approach greatly enhances sustainability, a crucial factor in the constantly changing landscape of CMSSs. It does so by adeptly handling various data types and ensuring seamless integration among different system components. The introduced CMSS constitutes a novel approach in designing a viable, flexible, manageable, efficient, scalable DS system.