# CvAMoS--Event Abstraction Using Contextual Information
## 6 Discussion  
The approach presented in the paper aims at abstracting activities that are recorded in an event log in the form of raw sensor data. Activities are recurring sequences of events, which are distinguished by the state of the environment when they are performed. The algorithm is compared to another approach from the literature, the event abstraction using LPM. In order to reply to the research questions introduced in Section 1, two evaluations are performed: the first on a synthetic dataset and the second one on a real dataset. The evaluation using synthetic data consists of a controlled experiment with the objective of verifying the ability of the approach to identify significant sequences of events, which also include the context. In the first experiment, the findings revealed that CvAMoS was able to recognize almost the entire set of the target patterns even if the precision was not perfect as the algorithm also identified non-expected motifs. On the contrary, LPM identified a very small percentage of the target patterns. In the second experiment on the synthetic dataset, only CvAMoS was applied. Additionally, in this case, the algorithm correctly recognized the target patterns, distinguishing them by the context. Unlike the previous experiment, the algorithm was very precise. In particular, it perfectly recognized the patterns in five cases out of fourteen, without producing other non-fitting motifs. This is an important finding in the evaluation of the approach, as it demonstrates the capability of the algorithm in identifying motifs based on the context. The correct identification of the patterns leads to a positive response for RQ1, which questions the approach with respect to its ability to identify meaningful patterns. In this case, the meaningful patterns are the given target patterns. What is more, the algorithm correctly distinguished the patterns based on their context, confirming the ability of the algorithm to deal with the context, i.e., RQ2. As a second evaluation, we assessed the performances of the algorithm on a real dataset, i.e., the CASAS dataset. Since no ground truth is given regarding the context, we cannot draw any conclusions about RQ2, even if the verified motifs returned by CvAMoS show different contextual attributes. Table 5 depicts the result of the abstraction. Given the possibility to filter the identified patterns in the event log, we can obtain an abstracted log  
Figure 5: Comparison between a motif obtained by CvAMoS and a tentative to reconstruct it by using the patterns obtained by LPM. (**a**) Motif obtained by CvAMoS. (**b**) Patterns obtained by LPM. which contains all the aggregated patterns. The abstraction generated 61 different activity labels obtained in 27 iterations. As can be noticed from the colored bars, a considerable part of the original event log is abstracted in motifs, meaning that recurring sub-traces are simplified and replaced with an activity label. Considering that the algorithm replaced the identified patterns with activity labels, obtaining an abstracted event log, RQ3 can be positively answered. Furthermore, the obtained motifs are explicable and can be interpreted as higher-level activities. When comparing our results to those obtained by LPM, it is clear the difference in the expressiveness of the patterns. In fact, LPM only identified models representing the alternation between two or three sensors, and part of them are duplicates. Overall, these findings are in accordance with the drawbacks of the LPM algorithm [26]. The result validates the capability of CvAMoS to satisfy RQ1 as well. The last aspect to consider is the computation time. The time collected in both cases relates only to the execution of the algorithm and does not include the time required for an expert to supervise the results, filter, and label them. What is more, the computation time also depends on the number of candidates to be evaluated. In this version of CvAMoS, we tried to reduce this set by considering only observed sequences.