# A Data-Driven Approach to Discovering Process Choreography
## 5 Experimentation
### Experiment Results
In event logs with a low level of complexity and a low number of cases and events, the approach identified the message-type tasks in approximately 200 s, using the threshold \(U_{t}\) and \(U_{a}\) parameters presented in Table 3. For the collaborations of the Air Quality System scenario, which are characterized by real-life event logs with a high number of cases and events, as well as a medium-high complexity, the algorithm required 105,371 and 83,419 s for collaborations IAQM-SAS and IAQM-RMS, respectively. The time consumed in this scenario encouraged us to continue experimenting with large event logs with greater complexity. The experimentation was carried out on a personal computer with an AMD Ryzen 5 3400G processor, 3700 MHz, 4 cores, with Radeon Vega Graphics, 16 Gb RAM, and 1 Tb SDD with Windows 10 operating system and the Python 3.7 programming language. The time consumed by the approach to the processing of message \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Scenario** & **Collaboration** & **RP** & **RR** & **FsR** & **Parameters** & **Time (s)** \\ \hline Air quality system & IAQM-SAS & 1 & 1 & 1 & \(U_{t}\geq 0.5\), \(U_{a}\geq 0.98\) & 105,371 \\  & IAQM-RMS & 1 & 1 & 1 & \(U_{t}\geq 0.5\), \(U_{a}\geq 0.98\) & 83,419 \\ Healthcare & Patient-Gynecologist & 1 & 1 & 1 & \(U_{t}\geq 0.2\), \(U_{a}\geq 0.96\) & 60 \\  & Gynecologist-Laboratory & 1 & 0.5 & 0.66 & \(U_{t}\geq 0.2\), \(U_{a}\geq 0.50\) & 46 \\  & Hospital-Gynecologist & 0.5 & 0.5 & 0.5 & \(U_{t}\geq 0.2\), \(U_{a}\geq 0.96\) & 57 \\  & Hospital-Patient & – & – & – & \(U_{t}\geq\
eg,U_{a}\geq-\) & 21 \\ Travel agency & Customer-Travel agency & 1 & 1 & 1 & \(U_{t}\geq 0.2\), \(U_{a}\geq 0.89\) & 158 \\ Purchase order & M-Repair-M-Parts & 0.83 & 1 & 0.90 & \(U_{t}\geq 0.5\), \(U_{a}\geq 0.9\) & 203 \\ Transfer of goods & Sender and Buyer-Consigner & 0.8 & 1 & 0.88 & \(U_{t}\geq 0.5\), \(U_{a}\geq 0.6\) & 1 \\  & Carrier-Consigner & 0.8 & 0.8 & 0.8 & \(U_{t}\geq 0.5\), \(U_{a}\geq 0.6\) & 1 \\  & Consigner-Shipper & 0.75 & 0.75 & 0.75 & \(U_{t}\geq 0.5\), \(U_{a}\geq 0.6\) & 1 \\  & Carrier-Shipper & 1 & 1 & 1 & \(U_{t}\geq 0.5\), \(U_{a}\geq 0.4\) & 1 \\ Manufacturing process & Bulkbuyer-Manufacturer & 1 & 1 & 1 & \(U_{t}\geq 0.2\), \(U_{a}\geq 0.9\) & 1 \\  & Supplier B-Manufacturer & 1 & 1 & 1 & \(U_{t}\geq 0.2\), \(U_{a}\geq 0.95\) & 1 \\  & Middleman-Manufacturer & 1 & 1 & 1 & \(U_{t}\geq 0.2\), \(U_{a}\geq 0.98\) & 1 \\  & Middleman-Special Carrier & 1 & 1 & 1 & \(U_{t}\geq 0.2\), \(U_{a}\geq 0.9\) & 1 \\  & Middleman-Supplier A & 1 & 1 & 1 & \(U_{t}\geq 0.2\), \(U_{a}\geq 0.9\) & 1 \\  & Special Carrier-Supplier A & 0.28 & 1 & 0.43 & \(U_{t}\geq 0.2\), \(U_{a}\geq 0.9\) & 1 \\  & Supplier A-Manufacturer & 1 & 1 & 1 & \(U_{t}\geq 0.2\), \(U_{a}\geq 0.98\) & 2 \\  & Special Carrier-Manufacturer & 1 & 1 & 1 & \(U_{t}\geq 0.2\), \(U_{a}\geq 0.98\) & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the evaluation of the proposed methods to identify the message-type tasks and their subtype. task identification in the event logs of the Air Quality System scenario could be decreased, and the first action would be focused on improving the processing characteristics of the computing equipment. Table 4 shows the average value of the metrics \(RP\), \(RR\), and \(FsR\) per scenario in the identification of message-type events. These averages are derived from the values achieved for the metrics presented in Table 3. The overall evaluation of this task was 0.86 for the \(RP\) metric, 0.89 for the \(RR\) metric, and a \(FsR\) of 0.86. The results presented are acceptable, indicating that the approach had a performance greater than 89% in the task of finding message-type events in an inter-organizational scenario; that is, it correctly identified (89%) the interaction of messages between the participants, which is the basis for the specification of the process choreography. Furthermore, 86% of the relationships found were genuinely relevant.