## 5 Results
### Experimental Analysis
#### 5.2.2 Experiment 2: Comparing the Speeds of the Different Enhancements  
The time it takes for a compressor to compress a file is also an important factor. As a result of this, we found it important to compare the run times of the different enhancements. We also analyzed the time our algorithm took to decompress the data. We assume that the templates are present, so the evaluation of the speed needed to generate the templates is not part of this paper. The results can be seen in Figures 11-14. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MoLFI** & **Small Dataset** & **Mid Dataset** & **Large Dataset** & **Big Dataset** \\ \hline Huff & & & & \\ \hline Template dictionary & 3.832 KB & 4.45 KB & 4.606 KB & 4.461 KB \\ Huffman codec (WPE) & 1.068 KB & 1.125 KB & 1.118 KB & 1.134 KB \\ Compressed file & 313.133 KB & 986.065 KB & 2455.239 KB & 5550.092 KB \\ Overall & 318.033 KB & 991.64 KB & 2460.963 KB & 5555.687 KB \\ \hline Enh & & & & \\ \hline Template dictionary & 3.832 KB & 4.45 KB & 4.606 KB & 4.461 KB \\ Parameter dictionary & 6.348 KB & 21.743 KB & 24.448 KB & 25.206 KB \\ Compressed file & 288.102 KB & 854.308 KB & 1996.157 KB & 4522.154 KB \\ Overall & 298.282 KB & 880.501 KB & 2025.211 KB & 4551.821 KB \\ \hline Enh Huff & & & & \\ \hline Template dictionary & 3.832 KB & 4.45 KB & 4.606 KB & 4.461 KB \\ Parameter dictionary & 6.348 KB & 21.743 KB & 24.448 KB & 25.206 KB \\ Huffman codec & 0.362 KB & 0.362 KB & 0.362 KB & 0.362 KB \\ Compressed file & 105.48 KB & 303.202 KB & 699.61 KB & 1606.879 KB \\ Overall & 116.022 KB & 329.757 KB & 729.026 KB & 1636.908 KB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 11: Speeds of the different enhancements used on the Small dataset. Figure 14: Speeds of the different enhancements used on the Big dataset. Figure 12: Speeds of the different enhancements used on the Mid dataset. Figure 13: Speeds of the different enhancements used on the Large dataset. It can be seen that the creation of a parameter dictionary does not take much time, while the Huffman coding proves to be slower, especially when any kind of character could occur in the file to be compressed, so it takes more time to find the representation in the codec. In the case of the parameter dictionary approach, the decompression time is just slightly more than the time it takes to compress the file. The same cannot be said for the approaches that use Huffman coding, since it takes at least twice as much time to decompress the file when this method is involved. The MoLFI version is somewhat faster than the IPLoM one. 2.3 Experiment 3: Comparing the Compression Rates of the New Enhanced Algorithm and General Compressors  
In this experiment, we wanted to compare the compression rate achieved by our parameter dictionary and Huffman coding technique with the compression rates of general compressors. The results can be seen in Figures 15-18. Out of the three investigated general compressors, LZMA has the highest compression rate, with an average of 98.97%, while PPMd has the least, with 97% on average. Our enhanced algorithm achieved 91.49% on average when used with IPLoM and 92.11% in the case of MoLFI. We also wanted to measure the compression rates on larger data, with the size being measured in gigabytes. We only investigated the compression rates of MoLFI, since it had the highest compression rate on the previous datasets. For this purpose, we introduced four new datasets, A, B, C, and D, with the sizes of 2 GB, 5 GB, 10 GB, and 50 GB, respectively. The compression rates achieved are shown in Table 3 and Figures 19-22. Figure 19: The comparison of compression rates of the enhanced algorithm and general compressors on the A dataset. Figure 20: The comparison of compression of the enhanced algorithm and general compressors on the B dataset. Figure 21: The comparison of compression rates of the enhanced algorithm and general compressors on the C dataset.