## 4 Results
### Experimental Results
#### 4.3.2 Real Log
(**a**) Direct following graph of filtered event log containing eight activities. (**b**) Direct following graph of filtered event log containing seven activities. Figure 3: Direct following graph for Sepsis-direct. (**a**) Direct following graph of filtered event log containing nine activities. (**b**) Direct following graph of filtered event log containing seven activities. activity filtering approach (Indirect), indirect entropy-based activity filtering approach with Laplace smoothing (Indirect(\(\alpha=1/\left|\right.\left.\mathrm{A}\right|\))), and the least-frequent-first chaotic activity filtering approach Infrequent. The following conclusions can be drawn by observing the accuracy comparison results of the following chaotic activity filtering methods. From the experimental results in Figure 5a to Figure 7a, it can be concluded that, first of all, the quality of the process model is gradually improved as the number of activities in the event log decreases. Moreover, the accuracy of the chaotic activity filtering method is closely related to the event log itself, that is, for different event logs, the accuracy comparison results presented by the chaotic activity filtering method are different. Secondly, the chaotic activity filtering method proposed in this paper is superior to the frequency-based chaotic activity filtering method in terms of accuracy. This method has certain advantages in the accuracy of chaotic activity filtering, and can maintain the integrity of the filtered event log to a certain extent. The traditional chaotic activity filtering method delletes the identified chaotic activities from the log successively until there are two activities in the log, so it is difficult to ensure the integrity of the filtered log. However, the methods proposed in this paper identify and filter chaotic activities in logs by using the method of calculating thresholds, which can ensure that there are still more activities in event logs after filtering, so that the logs can maintain certain integrity after filtering, and thus maintain the integrity of the process model. Finally, it can be observed that in some event logs, compared with the traditional chaotic activity filtering method, the accuracy curve of the process model after filtering can reach a stable state faster. Therefore, the filtering method proposed in this paper can obtain a suitable process model at a faster speed. As for the time performance of chaotic activity filtering approaches, the time required to filter real event logs by different chaotic activity filtering approaches is calculated, and the time performance of chaotic activity filtering approaches proposed in this paper is compared with four approaches proposed in reference [13] and least-frequent-first chaotic activity filtering approaches. According to the experimental results shown in Figure 5b to Figure 7b, the following conclusions can be drawn: First, for different real event logs, the running time of the chaotic activity filtering method is related to the number of activities in the log and the occurrence frequency of activities. Moreover, the running time of the Indirect filtering method is higher than that of the Direct filtering method, and the running time of the filtering method with Laplace smoothing (Direct(\(\alpha=1/\left|\right.\mathrm{A}\left|\right.\)), Indirect(\(\alpha=1/\left|\right.\mathrm{A}\left|\right.\))) is higher than that of the filtering method without Laplace smoothing (direct, indirect). Secondly, through  
Figure 7: Experimental result of Environmental Permit. (**a**) Model quality comparison. (**b**) running time comparison. observation, it can be seen that the running time of the direct chaotic activity filtering method proposed in this paper is significantly less than that of the traditional frequency-based chaotic activity filtering method and the four chaotic activity filtering methods based on activity entropy.