# 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
``` <?xmlversion=\"1.0\"encoding=\"UTF-8\"> <log> <trace> <stringkey=\"IdCase\"value=\"62576 aab70c34\"/> <event> <stringkey=\"IdEvent\"value=\"11009\"/> <stringkey=\"conceptname\"value=\"Insert_Data_Package\"/> <datekey=\"time:stamp\"value=\"13-04-2022_19:28:27\"/> <stringkey=\"dataIO\"value=\"Insert_Data_Package\"/> <stringkey=\"resource\"value=\"Web_Service_C/> <stringkey=\"participant\"value=\"Request_Value=\"Web_Service_C/> <stringkey=\"participant\"value=\"Request_Value=\"Web_Assagement_Service\"/> <event> <stringkey=\"IdEvent\"value=\"11010\"/> <stringkey=\"conceptname\"value=\"False\"response_Data_Package\"/> <datekey=\"time:stamp\"value=\"13-04-2022_19:28:27\"/> <stringkey=\"dataIO\"value=\"/> <stringkey=\"resource\"value=\"Web_Service_C/> <stringkey=\"participant\"value=\"Request_Value=\"Web_Service_C/> </event> <stringkey=\"IdEvent\"value=\"11014\"/> <stringkey=\"conceptname\"value=\"Confirm_Correct_Insertion\"/> <datekey=\"time:stamp\"value=\"13-04-2022_19:28:27\"/> <stringkey=\"dataIO\"value=\"Comfort_Insertion\"/



<event>  <string key=\"IdEvent\" value=\"11015\"/>  <string key=\"conceptname\" value=\"Inform_Shutdown_AQ_System\"/>  <date key=\"time:stamp\" value=\"13-04-2022_19:28:27\"/>  <string key=\"dataIO\" value=\"Inform_Shutdown_AQ_System\"/>  <string key=\"resource\" value=\"Web_Service_C/>  <string key=\"participant\" value=\"Repository_Management_Service\"/>  </event>  </trace> <log>

#### 4.2.4 Evaluation

Table 2 presents the quality metrics calculated for each event log. The scores are also visualized with grades: low under 0.45, medium between 0.45 to 0.75, and high above 0.75, respectively. The accuracy measure is calculated by considering four elements: infrequent event ordering, overlapping events per resource at the trace level, future entry, and precision at the event level. All three event logs achieve a 0.92 value for the accuracy metric, which is a very acceptable performance for an event log. The completeness metric as a quality parameter is calculated as follows: at the event level through the identification if there is any time-stamp missing in the events; at the activity level, it is verified if there are missing events; at the trace level, missing activities are detected and counted, and at the log level, missing traces are detected in the event log. The value obtained in evaluating the three event logs in the completeness metric is 0.74, with a very close high quality value. Consistency measure is examined at the activity, trace, and log levels to confirm that you have the same granularity at each level. A mixed granularity can lead to an incorrect order of activities. The evaluation of the three event records confirmed that the granularity is consistent at each level, with a very high value close to 1 (see Table 2). Uniqueness is evaluated at the activity, trace, and log levels, identifying the existence of unwanted duplicates at each level of abstraction. In the case of the IoT Air Quality Monitor event log, duplicate traces and activities were identified, causing the quality of this event log to be close to low, with a value of 0.47. Duplicate traces are identified in the Repository Management Service and System Access Service event logs, causing a medium quality grade with a value of 0.66, which is an acceptable value for the event log quality using the uniqueness metric. Table 3 shows the metrics of the three event logs generated using the proposed approach. The IoT Air Quality Monitor participant event log is the largest, containing 511,049 events, with 24 unique activities within 9180 cases. The Repository Management Service participant event log achieved 215,651 events with 8 unique activities. This event log contains a high event total because many interactions are performed between these two participants in the business process behavior. The System Access Service participant event log recorded 64,260 events with 7 unique activities.