## 1 Introduction
Additionally, these data often exhibit increased internal complexity and particular diversity of information required in missions, which further complicates the duty of operational officers who process these data [19]. The rapid dynamic changes of specific data (in terms of context, type, data structure, format, etc.) may present difficulties in being updated, potentially leading to considerable decision-making challenges for operational professionals [20]. Often, the presence of multiple autonomous entities producing distinct datasets leads to substantial issues concerning the internal coherence and compatibility of targeted data sub-sets. As a result, this inability to achieve internal interoperability and/or compatibility generally hampers the utilization of these data for operational procedures [21]. The bulk of information needed for DSSs is likely already developed in segments and of satisfactory quality, originating from multiple relevant authorities (e.g., governmental institutions, organizations, etc.) [22]. The way in which specific operational proceduresare designed and executed by Subject Matter Experts (SMEs) has significant similarities among such procedures regardless of the kind of mission. The aforementioned SMEs are highly knowledgeable trained officials performing specialized functions in given processes. Nonetheless, the ability to develop solutions may markedly differ among operational officials; the fundamental principles of human reasoning used in designing missions are common among all operational officials. The procedures that any participant actor applies in any kind of mission present similarities (common segments). These common procedure segments have already been standardized or even established as common procedure templates by operational officials for a considerable number of missions [23] (see Appendix A). However, to fully exploit the potential offered by the aforementioned similarities, in this work it was deemed necessary that certain actions ought to be taken as follows: (a) _Constructing data cores_ which are organized in a systematic way to support complex operational procedures and (b) _developing a general adaptable and adjustable_ framework of methodologies, based on the aforementioned data cores, to support a considerable thematic range of different operational procedures. This research introduces specific methodologies and techniques for organizing and handling large-scale information in DSSs for complex missions with the goal of resolving complexities associated with diversity, polymorphism, and information volume. The aforementioned combinatorial solutions include:  
* _1 st Methodology:_ Use of information organization of the incoming data in the CMS system, based on purpose. This methodology emphasizes purpose-driven information organization. Aligning operational procedures with specific purposes aids efficient information organization correlating each action with the data required for mission support [24]. * _2 nd Methodology:_ Use of thematic decomposition of complex operational procedures based on their thematic content, as well as the geographical area where they take place [25]. * _3 rd Methodology:_ Use of a novel mission design algorithm for identifying key intermediate objectives, enabling the CMSS to easily and flexibly handle any kind of complex operational procedure, following crucial intermediate steps in order to accomplish a mission successfully. * _4 th Methodology:_ Use of a novel system architecture called a \"teleological structure\" or \"thematic content map\". The aforementioned architecture ensures the sustainability of a complex system because it resembles the way in which the human mind works, correlating each subject or issue that it is called upon to address with the corresponding sub-sets of data and computational methods required to solve a particular problem or issue. Components of the teleological methodology are used in the three aforementioned methodologies by appropriate adaptation to CMSSs.