# Semi-Automated Approach for Building Event Logs for Process Mining from Relational Database
## 4 Results
### Construction of the Event Log
#### 4.2.3 Event Log Generated
The lower number of events with the same total cases than the other event logs is since the business goal defined in the business process executed by the participant is focused on managing access to the cloud storage system from a request from the IoT Air Quality Monitor participant. The \"ATC\" column of Table 3 shows the average time (seconds) required to execute one case for each event log, e.g., in the System Access Service participant event log, the time consumed was approximately 1 s. The \"ATE\" column shows the average time consumed between the event execution; in the Repository Management Service event log is observed that more \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Event Log** & **Accuracy** & **Completeness** & **Consistency** & **Uniqueness** \\ \hline IoT Air Quality Monitor & 0.922 (high) & 0.749 (medium) & 0.984 (high) & 0.472 (medium) \\ Repository Management Service & 0.920 (high) & 0.740 (medium) & 0.980 (high) & 0.666 (medium) \\ System Access Service & 0.922 (high) & 0.749 (medium) & 0.990 (high) & 0.666 (medium) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quality metrics calculated for each of the event logs built. time is required for the event execution (15 s). In this case, it can be considered correct due to the business goal defined for the business process that generates this event log. Figure 3 shows an analysis of the main process variants retrieved from the event log of the IoT Air Quality Monitor participant. Through the process variants, characteristics found in the traces of an event log can be identified [38], allowing one to know the different directly-follows relations, i.e., discovering a sequence of specific activities through the flow of the business process. In the first map of the process variant presented in Figure 3 (from top to bottom), it is observed that a sequence of SDILS-VSD activities, executed twice in a row, may correspond to a loop within the flow of the process. Compared with the second map of process variants, it is observed that the first 15 activities are similar to the two process variants. However, in the second variant, the CDS-ECD sequence activities were executed on two continuous occasions (see that there is a loop in this part of the process). The SDILS-VSD sequence of activities was only performed once, with respect to the first variant of the process. The differences between the process variants are presented in the second part of the process map, showing various loops at different stages of the business process flow. Therefore, it allows an execution pattern to be identified from the process variables. Figure 4 shows the execution frequency of each activity per participant. In the IoT Air Quality Monitor event log, the activities \"Collect data sensors\", \"Evaluate collected data\", \"Save data in local storage\", and \"Verify saved data\" present a higher frequency percentage. On the other hand, in the Repository Management Service participant event log, the activities \"Confirm IP address assignment\", and \"Inform shutdown AQ system\" have the lowest frequency percentage of 4%. Finally, in the event log of the system access service participant, the seven activities have the same appearance rate within the record. The discovery of the business process model is one of the core tasks in process mining. Therefore, in our proposed approach, the Split-miner [26] and DFG [39] algorithms were applied to discover the business process model for each participant, allowing us to compare and verify the completeness of the event logs generated. The DFG algorithm was implemented using the PM4 PY tool. In Figure 5, the graph discovered from the _IoT Air Quality Monitor_ event log using the DFG algorithm is shown (see Definition 5). This graph is composed of 24 activities. The highlighted activities represent the behavior in the business process of collecting data through the sensors.