# CvAMoS--Event Abstraction Using Contextual Information
## 2 Related Work  
Event abstraction is a well-known research problem, not only in the field of process mining. There are numerous works that approach the problem from different angles and tackle it with different techniques. A non-exhaustive review of the literature is presented by [PERSON] et al. [10] in which they propose a taxonomy of event abstraction methods. Additionally, [PERSON] et al. [11] discuss the importance of bridging the gap between the granularity at which data are recorded and at which they should be analyzed. They present all the steps from event extraction and correlation to abstraction, and they review several related approaches. This highlights the importance of developing effective methods to abstract events in order to better understand and analyze processes. However, the abstraction is still an open challenge. In the context of this paper, we only focus on approaches suitable for the application to sensor data that describe human behavior processes.  
The first point to discuss regards the involvement of an expert in the abstraction. Expert knowledge is crucial to obtain information on the process and on the activities carried out. When the expert takes an active role in the abstraction, we talk about supervised approaches. Supervised techniques require a deep understanding of the data by an expert, who must be able to provide detailed information on the collected process. The recognition process consists in the provisioning of an approximate mapping between events and labeled activities, then the algorithm translates the actual observed events according to the map. However, when the process to be analyzed refers to human behavior and is collected by sensors, high variability and complexity characterize the data, making the intervention of an expert unsuitable for the manual labeling of the activities. Nonetheless, the knowledge of the expert is necessary to have information on sensory correlations, to identify groups of sensors involved in the execution of a specific activity, for the evaluation and verification of the correctness of the result. What is more, in a supervised approach, the set of activities to identify is limited to the specified one. Consequently, researchers focused on other methodologies to abstract events, oriented towards a \"bottom-up approach\", where the set of possible activities is elicited directly from the data, enabling the discovery of activities that were not foreseen beforehand. If the abstraction starts from the data, the set of identifiable activities is larger and more pertaining to the reality observed in the log. On the other side, unsupervised approaches rely completely on the algorithm for the identification of patterns. The main drawback is that they usually generate a massive amount of possible patterns, without providing any additional information. Since the generation did not leverage domain knowledge, many of the patterns could be not of interest, meaningless,or difficult to interpret. Given that, the role of the expert should be deemed a fundamental part in the abstraction, but not the main driver.  
Two categories of approaches can be identified in the literature, and are the model- and trace-based abstractions. The model-based aims to identify activities in the form of process models, while the trace-based focuses on the identification of activities at the trace-level.