# CvAMoS--Event Abstraction Using Contextual Information
## 7 Conclusions and Future Work  
In this paper, we present a trace-based approach to perform event abstraction. The novelty of the approach lies in its ability to deal with the context during the identification of the patterns. CvAMoS is able to identify common sequences of events in an event log, up to a certain dissimilarity, and distinguish them based on contextual attributes. Contextual information is obtained using sensors that capture the state of the environment while the activity in the pattern is being performed. This information is already included in the raw event log, usually in the form of continuous sensor values (e.g., the temperature sensor). The performance of the algorithm is verified by a dual assessment, the first based on a synthetic dataset and the second on a real dataset. The findings revealed that the algorithm is capable of identifying a set of candidate motifs, without providing prior knowledge. The identified motifs are structured enough to be self-explainable or that can be easily interpreted (RQ1). What is more, the key factor of this paper is the ability of the algorithm to discriminate motifs based on contextual attributes (RQ2). In fact, the algorithm constructs the set of candidate motifs by considering contextual variables, and then considers the context also in the evaluation of the set of motifs. The verified motifs can be filtered in the event log to obtain an abstracted event log (RQ3). Lastly, the algorithm showed good time performance in both evaluations, especially when applied to a real dataset. The algorithm has linear complexity, meaning that the time required to execute increases according to the size of the event log and to the size of the set of candidate motifs. Despite this, the algorithm can be employed on large datasets and maintain reliable performance (RQ4). All things considered, the experiments and the comparison suggest that CvAMoS is a valid technique that can be used in the processing of event logs, to translate low-level events into higher-level meaningful activities, while considering the context.  
As a future work, we plan to include a visualization tool for the algorithm, as it simplifies the abstraction as well as reduces the processing time of filtering and labeling. The visualization can also support the evaluation of the context. Moreover, a more refined way to check the contextual attribute distance may be implemented, since it now supports only a few aggregation operators. In addition, it would be interesting to investigate the performances of the approach in different application scenarios.  
Conceptualization, G.D.F. and A.B.; methodology, G.D.F. and A.B.; software, G.D.F. and A.B.; validation, G.D.F. and A.B.; formal analysis, G.D.F. and A.B.; investigation, G.D.F. and A.B.; resources, G.D.F. and A.B.; data curation, G.D.F.; writing-original draft preparation, G.D.F.; writing-review and editing, G.D.F. and A.B.; visualization, G.D.F. and A.B.; supervision, A.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The approach presented in this paper, CvAMoS, is implemented as a Java application. The source code is available in the following repository: [[https://github.com/gemmadifederico/Cvamos](https://github.com/gemmadifederico/Cvamos)]([https://github.com/gemmadifederico/Cvamos](https://github.com/gemmadifederico/Cvamos)) (accessed on 9 February 2023). All the datasets used during the evaluation of the approach can be found at [[https://doi.org/10.5281/zenodo.7613326](https://doi.org/10.5281/zenodo.7613326)]([https://doi.org/10.5281/zenodo.7613326](https://doi.org/10.5281/zenodo.7613326)), together with the detailed results.  
The authors declare no conflict of interest.