## 5 Results
### Experimental Analysis
#### 5.2.4 Experiment 4: Comparing the Speeds of the New Enhanced Algorithm and General Compressors  
As mentioned before, the time an algorithm takes to compress the data is also an important measure. As a result of this, we wanted to compare the speeds of the general compressors against our enhanced algorithm. Both the compression and decompression times are analyzed. The time it takes to compress the used datasets are visualized in Figures 23-26. Figure 23: The speeds of the enhanced algorithm and general compressors on the Small dataset. Figure 22: The comparison of compression rates of the enhanced algorithm and general compressors on the D dataset. Figure 24: The speeds of the enhanced algorithm and general compressors on the Mid dataset. Figure 25: The speeds of the enhanced algorithm and general compressors on the Large dataset. Figure 26: The speeds of the enhanced algorithm and general compressors on the Big dataset. Based on the results, it can be said that general compressors take less time to compress data than our enhanced algorithm. The explanation behind this is that the algorithm has to count and order the templates based on their occurrences, which takes more time than creating a stream of literals or allocating probabilities. It can also be seen that the more log entries that have to be counted, the more time our algorithm takes. Apart from this, we still consider our algorithm to be fast, since it compresses large amounts of data in just a few seconds. In terms of decompression time, our algorithm takes twice as much time as the general compressors, since it has to look up two dictionaries to decode the log messages. Aside from that, our algorithm is slower to decompress than general compressors; it only needs seconds in the case of the investigated datasets. The MoLFI variant of our enhanced algorithm is slightly faster in terms of both compression and decompression. 2.5 Experiment 5: Comparing the Compression Rates of the Joint Use of the New Enhanced Algorithm and General Compressors  
In this experiment, we wanted to investigate if the compression rate can be further improved if we use our enhanced algorithm in conjunction with the general compressors. These compression rates can be seen in Figures 27-30. The use of a general compressor improves the compression rate of our enhanced algorithm by approximately 5%, but this rate still does not reach the compression rate of the single use of general compressors. Nonetheless, we would encourage the use of both our algorithm and traditional compressors, since they can function as a wrapper for our template- and parameter-dictionaries, codec, and compressed file. Out of the tried combinations, the MoLFI variant of our enhanced algorithm used alongside PPMd had the best compression rate with an average of 98.42%. We also wanted to investigate the compression rate of the joint use of the MoLFI version (since it has better rates) of our proposed method and general compressors on the previously mentioned datasets, A, B, C, and D. The results are shown in Figures 31-34. Figure 30: The compression rates of the joint use on the Big dataset. Figure 29: The compression rates of the joint use on the Large dataset. Figure 31: The compression rates of the joint use on the A dataset. Figure 32: The compression rates of the joint use on the B dataset. Figure 33: The compression rates of the joint use on the C dataset. It can be seen that, in the case of datasets with sizes in gigabytes, the joint use produces approximately 1% higher compression rates than the ones achieved by the single use of the general compressor in Section 5.2.3. 2.6 Experiment 6: Comparing the Speeds of the Joint Use of the New Enhanced Algorithm and General Compressors  
We also evaluated the compression and decompression times of the joint use of our algorithm and general compressors. The results can be seen in Figures 35-38. The use of a general compressor increases both the compression and decompression; however, this extra time is negligible.