# CvAMoS--Event Abstraction Using Contextual Information
## 1 Introduction  
Process mining [1] analyzes events that are logged during the execution of a business process, with the aim of gathering useful information and knowledge. Process discovery is the task of generating a process model, starting from an event log, such that it represents the process as it is collected in the event log. The main scope of the discovery is to obtain a process model that is understandable by the experts and that can be used to verify if the process executions are the expected ones. The quality of the process model is reflected in the quality of the data used for the derivation. When data in the event log are recorded in a too fine-grained fashion, the process model becomes too complex and no longer interpretable, and is thus less effective in extracting knowledge regarding the underlying process. For example, a process captured by the use of sensor systems. Events collected by sensor systems correspond to changes in the sensor states. Instead, the activities in a process model must be at a higher level of abstraction in order to be interpreted as the execution of a behavior performed by a human being rather than a sensory behavior. For this reason, sensor events cannot be interpreted by process experts and need to be abstracted in form of higher-level activities that exist at the process level. When the process refers to human behavior, the abstraction problem becomes more relevant and challenging. Sensor systems are a valuable means to automatically capturehuman-related processes. The way human beings behave can be intended as the execution of a series of activities in order to accomplish a goal [2], that fits in the definition of a business process [1]. However, there is a significant difference between a business process and a human-related process [3]. The former is typically structured and composed of a well-defined sequence of activities. The latter is unstructured [4], and activities are combined based on the specific instance, i.e., they are case-dependent. Indeed, the process participant decides the activities to be executed and their execution order, leading to a flexible structure of the process [5]. As a consequence, the set of activities is not completely known ex-ante, and the way the activities are combined varies from execution to execution and is influenced by the context. The context is a set of measures that defines the circumstances under which the process instance is executed. However, sensor systems provide environmental data that can represent the context, such as the temperature or the weather. On the one hand, we have information collected by sensor systems that are too fine-grained, while on the other hand, there is complexity in aggregating the events and understanding the activities performed. As mentioned above, including the context in the abstraction of activities is crucial as it allows us to distinguish behaviors based on the changes in the context. The ability to capture and analyze human-related processes represents a valuable opportunity in many fields. One of the areas that can benefit from these techniques is healthcare. Sensors can be used to monitor the treatment process of patients in hospitals to improve the quality of service, or they can be used to track the surgery process inside a hospital for better management [6]. Processes in hospitals are strongly influenced by contextual factors such as the weather or particular events. A different use case in the context of personalized healthcare is the analysis of the routine of patients suffering from dementia, to monitor the course of the disease [7]. The integration between sensor systems and process modeling is also recognized in other areas such as smart factories [8]. In particular, the integration between event processing and process management enables the monitoring and control of factory processes based on real-time data, where the process refers to both machine and manual activities.