## 4 Results
### Experimental Results
#### 4.3.1 Simulation Log  
The number of chaotic activities in the event log and the frequency of chaotic activities occurring in the log are uncertain. Therefore, inserting random positioning activities into the log can be used to simulate event logs containing chaotic activities. Random positioning activities are inserted randomly in the log, and random positioning activities with different frequencies can be obtained through different insertion methods. First, an experiment was conducted on the synthetic event log with \(k\) random positioning activities inserted, and the results were obtained as follows. Table 3 shows the experimental results obtained by inserting frequent random positioning activities into the log. The method _frequent(\(k\))_ was used to insert \(k\) frequent random positioning activities into the log. The frequency of the \(k\) random positioning activities is the frequency of the activity with the highest frequency in the log. Table 4 is the experimental result of inserting uniform random positioning activities into the log. The method _random(\(k\))_ of inserting uniform random positioning activities is used to insert \(k\) uniform random positioning activities into the log. For each of the \(k\) inserted random positioning activities, the frequency of occurrence is randomly selected from the uniform probability distribution. Table 5 showsthe experimental results obtained by inserting infrequent random positioning activities into the log. The infrequent random positioning activity insertion method _infrequent(k)_ is used to insert \(k\) infrequent random positioning activities into the log. The occurrence frequency of \(k\) random positioning activities is the occurrence frequency of the activity with the lowest occurrence frequency in the log. The experimental results show the error deletion rate generated in the process of using different filtering methods to filter chaotic activities in event logs, that is, the number of false deletion activities/the number of original activities, which is used to evaluate the effect of the filtering method. If the error deletion rate is high, it indicates that the method has a low accuracy in identifying chaotic activities. In the experiment, the Direct(drr) and Indirect(drr) approaches proposed in this paper were compared with the four methods proposed in reference [2] (direct entropy-based activity filtering approach Direct, direct entropy-based activity filtering approach with Laplace smoothing Direct(\(\alpha=1/\left|\,\mathrm{A}\,\right|\)), indirect entropy-based activity filtering approach Indirect, and indirect entropy-based activity filtering approach with Laplace smoothing Indirect(\(\alpha=1/\left|\,\mathrm{A}\,\right|\)) to compare the running time and error deletion rate of the approaches, that is, the accuracy of the filtering approaches. According to the deletion error rate generated by different filtering methods in the process of deleting all chaotic activities in logs, as shown in Table 3 to Table 5, firstly, infrequent random positioning activities have a greater impact on the accuracy of the filtering method for chaotic activities than frequent random positioning activities. Both the number and frequency of random location activities have a certain influence on the effect of the chaotic activity filtering algorithm. Secondly, the chaotic activity filtering method proposed in this paper can accurately distinguish the normal activity from the chaotic activity in the log, and the accuracy of the indirect filtering method proposed in this paper is better than that of the entropy-based indirect filtering method, and the error deletion rate of the chaotic activity filtering method proposed in this paper is basically the same as that of the direct filtering method based on entropy.