# FODIT: A Filter-Based Module for Optimizing Data Storage in B5G IoT Environments
## 2 Background
### Filters
The table structure is organized as follows: the first column identifies the storage option, the second column indicates the data volume stored, the \"Classical\" section shows: duplicate record percentages (4%, 5%, 7%, 8%), corresponding query response times for each percentage in the local DB. The \"FODIT\" column presents the total query response time when using the FODIT module. Figure 7: The figure displays classical insertion times compared with insertion times with the FODIT module for the DLT using 4%, 5%, 7%, and 8% of duplicate data. Please note that the AuxS time (olive) and Filter construction time (dark olive) is not visible in the picture due to its very small numerical value compared with the No duplicates time (light green). Figure 8 presents comparative graphs of query processing times between the classical approach and the FODIT module. The X-axis represents the data stored, including duplicate data, and the Y-axis represents the time required to perform the query process. The query process with the FODIT module is presented with the first three bar charts, while the classical query process is depicted with bar charts using 4%, 5%, 7%, and 8% of duplicate data. The graphs in Figure 9 show the query process on the DaaS cloud service. The first bar charts illustrate the query process with the FODIT module, and the rest of the bar charts present the classical query process using 4%, 5%, 7%, and 8% of duplicate data. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multicolumn{4}{c}{**Clinical**} & \multicolumn{4}{c}{**FODIT**} \\ \cline{2-11}  & **Dapidates (\#x)** & **Time** & **Dapidates (\#x)** & **Time** & **Dapidates (\#x)** & **Time** & **Dapidates (\#x)** & **Time** & **Time** \\ \hline \multirow{3}{*}{DB} & 10,000 & 10,400 & 3,6515 \(\times\) 10\({}^{-5}\) & 10,500 & 3,3140 \(\times\) 10\({}^{-5}\) & 10,700 & 4,4020 \(\times\) 10\({}^{-5}\) & 10,800 & 4,2160 \(\times\) 10\({}^{-5}\) & 3,0279 \(\times\) 10\({}^{-5}\) \\  & 100,000 & 10,4000 & 4,3263 \(\times\) 10\({}^{-5}\) & 10,500 & 4,3689 \(\times\) 10\({}^{-5}\) & 10,700 & 4,4642 \(\times\) 10\({}^{-5}\) & 10,800 & 4,3778 \(\times\) 10\({}^{-5}\) & 4,1246 \(\times\) 10\({}^{-5}\) \\  & 10,0000 & 1,40000 & 5,3515 \(\times\) 10\({}^{-5}\) & 1,6000 & 5,6527 \(\times\) 10\({}^{-5}\) & 1,0000 & 5,5906 \(\times\) 10\({}^{-5}\) & 1,0800 & 5,6528 \(\times\) 10\({}^{-5}\) & 5,2216 \(\times\) 10\({}^{-5}\) \\ \hline \multirow{3}{*}{DaaS} & 10,000 & 10,400 & 5,8275 \(\times\) 10\({}^{-5}\) & 10,500 & 5,4173 \(\times\) 10\({}^{-5}\) & 10,700 & 4,2130 \(\times\) 10\({}^{-5}\) & 10,800 & 6,2175 \(\times\) 10\({}^{-5}\) & 5,4602 \(\times\) 10\({}^{-5}\) \\  & 100,0000 & 10,4000 & 6,3546 \(\times\) 10\({}^{-5}\) & 10,500 & 6,3570 \(\times\) 10\({}^{-5}\) & 1,0000 & 6,6370 \(\times\) 10\({}^{-5}\) & 1,0800 & 6,6492 \(\times\) 10\({}^{-5}\) & 5,8055 \(\times\) 10\({}^{-5}\) \\  & 10,0000 & 1,40000 & 5,0418 \(\times\) 10\({}^{-5}\) & 1,0100 & 6,8197 \(\times\) 10\({}^{-5}\) & 1,0000 & 6,8453 \(\times\) 10\({}^{-5}\) & 1,0800 & 3,8378 \(\times\) 10\({}^{-5}\) & 6,6920 \(\times\) 10\({}^{-5}\) \\ \hline \multirow{3}{*}{DET} & 10,000 & 10,400 & 7,9960 \(\times\) 10\({}^{-5}\) & 10,500 & 7,9802 \(\times\) 10\({}^{-5}\) & 10,700 & 7,9805 \(\times\) 10\({}^{-5}\) & 10,800 & 7,9960 \(\times\) 10\({}^{-5}\) & 7,2866 \(\times\) 10\({}^{-5}\) \\  & 100,0000 &Figure 10 illustrates the query process in DLT. The bar charts show the query process with the FODIT module, and the next bar charts illustrate the query process using 4%, 5%, 7%, and 8% of duplicate data. The graphs in Figures 8-10 compare query performance across the three storage systems (DB, DaaS, and DLT), demonstrating that the FODIT module consistently outperforms classical query processing in terms of speed. The visualization clearly shows that data access via the FODIT module is significantly faster than traditional query methods. The key advantage of the FODIT module lies in its ability to eliminate unnecessary database searches.