# 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 value of 0.771 in the generalization metric indicates to what extent the discovered model can reproduce a future process behavior. Even when the accuracy of the process model is high (1.0), the generalization metric tells us that there may be difficulties when deciding on the path to follow at a decision point. On the other hand, the value 0.825 in the recall metric guarantees that the discovered model can accurately reproduce the cases recorded in the event log. ## 5 Conclusions

In this study, we proposed a framework to guide the construction of an event log from a relational database. The event log generated is based on the XES standard for use with process mining algorithms. The event log is constructed semi-automatically because, in the first stage, an analysis of the relational database tables and their link with the business process activities is carried out. In this analysis, experts are required at a technical and business level, with knowledge of the database structure and semantics of the data stored and its relationship with the business process. This part is fundamental so that a record of quality events is automatically built in the next stage. Correlation methods were defined at different levels of abstraction to extract the attribute values from the database tables, that is, at the case level, event, and event properties. For this, a set of rules were defined that allowed data extraction and converting them into the attribute values of the event log. The time consumed at the case and event level was analyzed from the event logs. In addition, process variants were retrieved for each event log, identifying activity sequence patterns throughout the business process flow. Finally, the quality of the constructed event logs was evaluated using accuracy, completeness, consistency, and uniqueness metrics, obtaining acceptable values in the two case studies presented. In addition, from the generated event logs, business process discovery techniques were implemented using DFG and split-miner algorithms. The discovered \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Process Model** & **Precision** & **Recall** & **Generalization** \\ \hline Patient Treatment Process & 1.0 & 0.825 & 0.771 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of the quality metrics for the patient treatment model discovered. Figure 11: BPMN process model discovered using Split-miner for _patient treatment_ participant. process models were measured to determine their quality, obtaining very acceptable values in the accuracy, recall, and generalization metrics. Conceptualization, J.D.H.-R. and E.T.-L.; methodology, J.D.H.-R. and E.T.-L.; software, J.D.H.-R.; validation, J.D.H.-R., E.T.-L., U.M.R.-A. and B.A.M.-H.; formal analysis, J.D.H.-R.; investigation, J.D.H.-R., E.T.-L., U.M.R.-A. and B.A.M.-H.; resources, E.T.-L.; data curation, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON] and [PERSON]; writing--review and editing, [PERSON], [PERSON], [PERSON] and [PERSON]; visualization, [PERSON]; funding acquisition, [PERSON]L. All authors have read and agreed to the published version of the manuscript. This research was partially funded by the Consejo Nacional de Ciencia y Tecnologia (CONACYT) under grant number 719519. Not applicable. The authors are grateful to the Autonomous University of Tamaulipas, Mexico for supporting this work. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:

Internet of Things

ExCensible Event Stream

DFG directly-follows graph

Business process model and notation

Average time of cases

Average time of events

Relational database

foreign key

primary key

## References

* [1] [PERSON]. Process Mining: A 360 Degree Overview. In _Process Mining Handbook_; [PERSON], [PERSON] Eds. ; Springer International Publishing: Cham, Switzerland, 2022; pp. 3-34. [CrossRef]
* [2] [PERSON]; [PERSON] From event logs to goals: A systematic literature review of goal-oriented process mining. _Requir. Eng._**2020**, _25_, 67-93.