## 3 Results
### Comparative Analysis of Proposed CMSS with Other Related Works/Existing DS System Methodologies  
Comparing the proposed CMSS framework with related works/existing DS system methodologies (see Section 1.1), a comprehensive comparative analysis could be conducted in order to shed more light on the framework's advantages and potential limitations. In Tables 1-3, an overview of certain major benefits and limitations for the proposed CMSS, the selected CMSS 1, and the selected CMSS 2, respectively, is presented. Tables 1-3 provide a comprehensive evaluation of our proposed CMSS, as well as the selected CMSS 1 and CMSS 2, highlighting several key aspects. The primary benefit of the proposed CMSS is the development of a diagrammatic mission transport algorithm using GraphML. The use of GraphML enables code generation and supports back annotation, meaning changes in code are reflected back directly within the diagram. Updating the graph by incorporating code changes enhances responsiveness and reduces the development time. On the other hand, CMSS 1, designed for SAR operations at sea, offers a substantial advantage through the integration of AIS information. This data system facilitates decision making by utilizing data to ensure that the most appropriate vessels are swiftly and efficiently deployed in time-sensitive search and rescue missions. Timely shipping information from AIS is essential in urgent scenarios requiring a prompt reaction. Finally, CMSS 2 shows exceptional flexibility in many operations, particularly in the use of drones. This system is adaptable and may be utilized for many missions including surveillance and SAR. Improving the ability to cover large areas is crucial for missions requiring rapid responses over wide territories. There is no automation in the interface with a user, like the existence of an LLM-powered chatbot. The extent of thematic breakdown in a complex mission is always contingent on the operational expertise and qualifications of the experts involved in mission planning. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Num** & **Advantages/Benefits** & **Potential Limitations** \\ \hline \multirow{4}{*}{1} & Systematically and specifically & The system cannot assess in its current \\  & preprocess input data before feeding & form the validity of input data, which \\  & them into the system, thereby easing & may lead to the Garbage In, Garbage \\  & the workload for operational experts. & Out (GIGO) phenomenon. \\  & & It is required to conduct \\  & Additional organization of the & comprehensive interviews with \\  & system’s input data into smaller and & operational specialists from diverse \\
2 & focused sub-sets tailored to the specific & fields to assess the data and program \\  & data requirements of actionable actors, & requirements that would support the \\  & thus preventing systems’ & \\  & information overload. & These interviews are instrumental in \\  & Development of a diagrammatic & gathering a wide variety of critical \\  & algorithm specifically for & operational information. \\
3 & & \\  & transportation mission planning using & the GraphML format. This method not \\
3 & only enables code generation but also & only enables code generation but also \\  & supports back annotation, meaning & changes in code are reflected back \\  & directly within the diagram. & \\  & Design the mission using the “divide & and conquer” principle, effectively \\
4 & breaking down complex tasks into & smaller, more manageable segments. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Major benefits and limitations of the proposed CMSS. The examination of the three CMSSs' distinct benefits helps in identifying the current applications in which each system excels. The proposed CMSS is very effective in complex and dynamic scenarios such as transportation mission planning because of its efficient planning, adaptability, and quick responsiveness.