# Analyzing Data Reduction Techniques: An Experimental Perspective
## 9 Discussion  
Having presented the results, we now focus on the interpretation and implications of the results. Before the analysis, it is critical to address some of the limitations of our \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Data Type**} & \multirow{2}{*}{**Name**} & **Original Size** & **Final Size** & **Percentage** & **Execution Time** & **Processing Time per MB** \\  & & **Size** & **Size** & **Variance** & **(ms)** & **(ms/MB) 2** \\ \hline \multirow{6}{*}{\begin{tabular}{c} Temporal, \\ Numeric \\ \end{tabular} } & Daily Minimum & \multirow{2}{*}{0.0558} & \multirow{2}{*}{0.0093} & \multirow{2}{*}{\begin{tabular}{c} \(-83.33\%\) \\ \end{tabular} } & \multirow{2}{*}{0.010} & \multirow{2}{*}{0.1792} \\  & Temperature in & Melbourne & & & & \\ \cline{1-1}  & Electric & & & & & \\ \cline{1-1}  & Electric & & & & & \\ \cline{1-1}  & Temperature in & 0.0558 & 0.0093 & \(-83.33\%\) & 0.010 & 0.1792 \\ \cline{1-1}  & Electric & & & & & \\ \cline{1-1}  & Production & 0.0730 & 0.0010 & \(-98.63\%\) & 0.001 & **0.0137** \\ \cline{1-1}  & Monthly Beer & & & & & \\ \cline{1-1}  & Production in & 0.0690 & 0.0010 & \(-98.55\%\) & 0.010 & 0.1449 \\ \cline{1-1}  & Austria & & & & & \\ \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} Numeric \\ Text \\ \end{tabular} } & Accelerometer & 3.7000 & 0.5682 & \(-84.64\%\) & 0.340 & 0.0919 \\ \cline{1-1}  & Smoke Detection & 5.8000 & 1.1703 & \(-79.82\%\) & 0.399 & 0.0688 \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{c} Temporal, \\ Text \\ \end{tabular} } & IoT Temperatures & 6.7000 & 0.8702 & \(-87.01\%\) & 0.640 & 0.0955 \\ \hline \hline \end{tabular}  
* The best result for percentage variance. 2 The best result is in bold.  
\end{table} 
Table 12: Delta Encoding results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Data Type**} & \multirow{2}{*}{**Name**} & **Original Size** & **Final Size** & **Percentage** & **Execution Time** & **Processing Time per MB** \\  & & **Size** & **Size** & **Variance** & **(ms)** & **(ms/MB) 2** \\ \hline \multirow{6}{*}{\begin{tabular}{c} Temporal, \\ Numeric \\ \end{tabular} } & Daily Minimum & \multirow{2}{*}{0.0558} & \multirow{2}{*}{0.0301} & \multirow{2}{*}{\begin{tabular}{c} \(-46.06\%\) \\ \end{tabular} } & \multirow{2}{*}{2.030} & \multirow{2}{*}{36.3799} \\  & Temperatures in & & & & \\ \cline{1-1}  & Melbourne & & & & \\ \cline{1-1}  & Electric & & & & & \\ \cline{1-1}  & Production & 0.0730 & 0.0401 & \(-45.07\%\) & 0.302 & **4.1370** \\ \cline{1-1}  & Monthly Beer & & & & & \\ \cline{1-1}  & Production in & 0.0690 & 0.0620 & \(-10.14\%\) & 0.330 & 4.7826 \\ \cline{1-1}  & Austria & & & & & \\ \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} Numeric \\ Text \\ \end{tabular} } & Accelerometer & 3.7000 & 3.6885 & \(-0.31\%\) & 285.220 & 77.0865 \\ \cline{1-1}  & Smoke Detection & 5.8000 & 4.7600 & \(-17.93\%\) & 422.130 & 72.7810 \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{c} Temporal, \\ Text \\ \end{tabular} } & IoT & \multirow{2}{*}{6.7000} & \multirow{2}{*}{5.4600} & \multirow{2}{*}{\(-18.51\%\)} & 693.040 & 103.4388 \\ \cline{1-1}  & Temperatures & & & & & \\ \hline \hline \end{tabular}  
* The best result for percentage variance. 2 The best result is in bold.  
\end{table} 
Table 13: BZip2 results. research. One limitation is the variable size of the datasets used in this study. Despite our research, we were unable to find publicly available datasets that met the desired size and specific requirements. This limitation required working with datasets of different sizes, which can introduce variability and potentially affect the comparison of results. However, we mitigated these challenges by normalizing the data (Equation (4)), enabling a more concise comparison despite the different dataset sizes.