# CvAMoS--Event Abstraction Using Contextual Information
## 2 Related Work
### Trace-Based Approaches  
On the other hand, trace-based approaches focus on the identification of commonalities between traces. [PERSON] et al. [17] propose a semi-supervised event abstraction technique, based on an interactive approach. To overcome the generalization problem, traces in the event log are visualized in a dotted chart format instead of a process model format. The user can detect and extract patterns of interest from the visualized log. The occurrences of these patterns can then be highlighted in the visualization in order to be explored and compared to other occurrences. The patterns can also be modified and extended using contextual knowledge. Another solution is proposed by [PERSON] et al. [18], a technique that aims to divide traces into batch sessions. Each trace is seen as a sequence of sessions of events. The sessions are then converted into vectors which abstract the behavior observed inside the sessions. The sessions are clustered based on similarity in order to identify common patterns and group similar activities together. The centroids of the clusters are visualized on a heatmap and used for naming the activity. Finally, they use the results of the clustering to abstract the original log by aggregating the events in each cluster into higher-level activities. Domain knowledge is used to adjust and support clusters labeling and to create an abstracted log. The approach has the drawback of relying on the choice of the session threshold parameter for trimming sessions, which can have significant impacts on the analysis by potentially leading to incorrect activity identification. Furthermore, the heatmap visualization does not clearly represent the activities, making the labeling process difficult.  
Despite advances in this field, the main problem of event abstraction remains. In particular, there is no dominant trend between model- and trace-based approaches. Although several studies have shown good results, there has been limited consideration of the level of granularity in the data source. Only a few studies in the literature have been able to effectively handle low-level sensor data (e.g., the work of [PERSON] et al. [15]).  
A first attempt to tackle this problem was made in an earlier version of the work presented in this paper. The work [9] aims to identify recurring sequences of events in the traces. The approach takes into account the variability of behaviors and the generalization problem, by implementing a distance function to verify the patterns. In addition, the distance function is enriched by a cost map that allows us to define the dependency degree between sensor identifiers. The approach is inspired by the bioinformatics field, from the well-known problem of protein sequence classification [19]. The protein sequence classification aims at identify the family of a given protein sequence based on its composition. However, the sequence does not always provide a clear indication of its family, and can be very large and complex. Similarly, in process mining, event abstraction involves simplifying large sequences of events by the identification of patterns in order to improve and understand the process. Nonetheless, none of the approaches mentioned above consider the context as part of the abstraction. In some works, the context is interpreted as the attributes of an activity in the event log, such as the time or the resource. Instead, the context is a broader dimension [20], which includes, among other things, the environment. Therefore, we propose an event abstraction technique that considers the state of the environment when the patterns were collected.