## 2 Materials and Methods
### Distinctive Traits of SSs--Difficulties Involved in Their Design  
A review of the literature has revealed that the majority of available systems supporting complex operational procedures do not serve as optimal models for constructing necessary data warehouses. This is due to a number of reasons. Firstly, their design relies on ad hoc _heuristic methods_ and internal structures that often lack suitability for handling large volumes of multi-thematic data [45]. In addition, the methodologies used for their development and implementation cannot be standardized or applied universally to similar cases and challenges [46]. Moreover, their data cores _lack the desired internal interoperability_[47]. Another key point is that they encounter _issues regarding updating and upgrading_ their internal procedures. They tend to become _unmanageable_ computationally or even _uncontrollable by the user_[48]. Equally important, as these systems scale in size and complexity, their sustainability diminishes significantly. They become practically non-expandable or modifiable in terms of their functionalities. As previously stated, these systems become overburdened with this growth and the only viable solution appears to be the complete replacement of the old system with a new one. This approach is excessively drastic and incurs prohibitive costs. Additionally, when numerous actors are involved in designing and executing complex operational processes, it is highly likely that challenges will emerge concerning aligning these procedures and their associated input data. In particular, in military operations, CMSSs struggle to receive large volumes of input data, despite the international efforts to achieve standardization for input data and interoperability among different data sources. Along with the above, in many cases, the presence of multiple independent data sources creates significant hurdles, leading to _internal interoperability issues_ or data sub-sets that clash, severely impeding or entirely obstructing their utilization in operational processes [49]. Above all, the data crucial for supporting complex missions, mainly tied to maps, tend to be _voluminous and complex_, adding layers of complexity to their management. Furthermore, the _constantly changing nature_ of specific data _complicates the updating process_, potentially causing disruptions in decision-making protocols whose efficient implementation is time dependent [50]. Last, but not least, given the vast range of complex missions, the data needed can be highly _diverse_ and _polymorphic_. This diversity poses substantial computational and algorithmic challenges in organizing these data within ISs [51].  
It should be emphasized that the current organization of the data is not due to problems or weaknesses of the IS's capabilities, but it is a remnant of the old traditional and convenient method of handling georeferenced information based absolutely on maps. In contrast, especially in human-centric missions, data organization in maps is not compatible with the human way of thinking, in which the data organization is based primarily on the subjects that should be solved and secondarily on maps. Therefore, the traditional method of organizing georeferenced information has a clear upper limit of usefulness because the volume and complexity are increasing.