# CvAMoS--Event Abstraction Using Contextual Information
## 5 Evaluation
### Evaluation on a Real Dataset
Each activity identifier is plotted using a different shade of purple. The second bar highlights in yellow shades the abstracted motifs (and their duration), in the corresponding abstracted trace. The other columns of the table report the amount of events for each trace in the original event log, and the amount of events not abstracted. The last column shows the percentage of the events in each trace that is abstracted in motifs. As can be noticed from the table, more than 50% of each trace is abstracted, in only 61 motifs, meaning that there is a recurrence of the motifs in the event log. C5 instead presents a low percentage of events abstracted, that is the 21.7%. However, even if the percentage seems low, the initial amount of events was very big, resulting in around 450 events grouped into motifs. In C12, the highest number of events have been abstracted, i.e., 595, while C14 is the most abstracted trace with only 90 events left as not abstracted. The last perspective to consider is the computation time. Table 6 reports the time needed for each run, in milliseconds. In each run, we divided the work between seven and nine threads. The total amount needed by the algorithm to run all the configurations is around 9.5 min. The findings suggest that there is no correlation between the computation time and the number of patterns returned as there are other variables to consider, comprising the motif length and distances, but mainly the attributes set. Algorithm 1 explains that a candidate motif is first checked per se, and then the attributes are verified. If a candidate motif does  
Figure 4: Verified motifs to which the same activity label has been assigned (Picture obtained from the Process Mining ToolKit). not contain any attributes, this second step is skipped, reducing the computation time. In addition, the size of the attribute set also impacts the processing times. It is important to note that the computation time reported in the table only includes the time the algorithm takes to identify and verify the motifs. The manual work of the expert, that is to go through the motifs, filter, and split them is not taken into account, but still remains in the range of minutes for each execution. The filtering of the motifs in the log, i.e., the replacement, is not monitored, as it always takes less than 2 s.  
Once obtained the results with CvAMoS, we tuned the parameters for the application of LPM accordingly. The LPM algorithm is included in the ProM tool (See [[https://promtools.org/](https://promtools.org/)]([https://promtools.org/](https://promtools.org/)) (accessed on 1 December 2022)). The abstraction consists of two steps: firstly, the plugin _Search for Local Process Models_ is used to derive the models, then each model needs to be converted from an Accepting Petri Net to a Petri Net and replaced in the log by the use of the _Log abstraction_ plugin. The searching algorithm was set to produce 61 models, with the number of transitions set to the maximum (i.e., 5). The minimum number of occurrences of each LPM in the log is set at 7, to allow the algorithm to discover a larger set of recurring and less recurring patterns. The algorithm took 19.5 min to execute and produce the result. The time only includes the identification of the models. The second step, which includes the verification, labeling, and filtering in the log, is not included. Even if the objective of LPM is to represent small patterns, between three and five activity nodes (i.e., transitions), the majority of the identified patterns are represented by the alternation between two sensors, thereby it is challenging to interpret them in the form of higher-level activities. We decided not to show the two abstracted event logs in the form of process models as the resulting model is strongly influenced by the choice of the discovery algorithm. As discussed in other works [3; 25] there is a lack of modeling languages able to faithfully represent human-related processes.