## 5 Discussion  
Throughout this project, numerous inquiries were raised. Some were related to the original research questions, while others steered the research in different directions. The outcome of this endeavor was a blend of explicit and tacit knowledge that provided valuable insights into implementation. In this section, a portion of this knowledge will be presented, with a primary focus on two key aspects. Firstly, the added value that this solution appears to offer in real-world use cases will be explored. It is important to note that the latter aspect is not substantiated by comparison with similar tools, mainly due to a lack of relevant information. However, it is based on the experience gained by the authors throughout the process. Secondly, the challenges encountered during the architecture testing phase will be discussed. _Added value._  
The tool has demonstrated the ability to efficiently handle a large number of simultaneous end users with minimal computational requirements. With an ever-growing need for access to important historical documents that stems from the fact that as students and scholars gradually gain access to them and employ them in their work, even more, such end-users require access to them, this is one of the most crucial performance aspects of a developed system like the one presented in this study. Although this is a crucial aspect, it is not the sole focus of the proposed architecture. The following is a set of supplementary features that could significantly impact the implementation and, more importantly, the scalability of similar tools. **Scalability. ** The proposed design is centered around an intermediary entity that acts as a bridge between the end user and the applications. This entity manages both the functionality and the service quality for the end user, offering pertinent responses as required without compromising its performance. Simultaneously, it imparts a distinctive feature to the system by enabling easy scalability either horizontally or vertically to cater to a larger user base. Through the utilization of additional computational power or by distributing \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **100 Users** & **1000 Users** & **10,000 Users** \\ \hline data\_received & 22 mb & 24 mb & 15 mb \\ data\_sent & 2.9 mb & 3.2 mb & 5.8 mb \\ http\_req\_blocked & 2.98 ms & 29.69 ms & 2.97 s \\ http\_req\_connecting & 782.92 μs & 29.05 ms & 2.62 s \\ http\_req\_duration & 302.61 ms & 3.68 ms & 6.24 s \\ http\_req\_failed & 0.00\% & 0.00\% & 71.99\% \\ http\_req\_receiving & 296.79 μs & 412.78 μs & 59.4 ms \\ http\_req\_sending & 9.89 μs & 830.77 μs & 242.91 ms \\ http\_req\_waiting & 302.3 ms & 3.68 s & 5.94 s \\ http\_req & 3389 & 3745 & 6798 \\ vus & 100 & 1 & 0 \\ vus\_max & 100 & 1000 & 10,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of scenario execution with 100 concurrent users. the workload among multiple entities, the design offers flexibility for small research teams or companies to implement and enhance the architecture based on usage. Consequently, the projected use (5-year scenario) does not necessarily have to align with the anticipated use during the initial phase, allowing financial planning to synchronize with expected revenue. Although the perceived scientific impact of this architectural enhancement may seem minimal, it is believed to significantly influence scientific advancements in this field by facilitating seamless collaboration and resource sharing among diverse research teams. **Extendibility**. The architecture's capability to easily connect various Graphical User Interfaces with different algorithmic models and databases, without requiring significant structural changes or extensive communication API refinements, closely resembles its previous characteristic. This feature enables tools created by different teams worldwide to seamlessly integrate with other databases and utilize models developed by various research teams with minimal effort.