# CvAMoS--Event Abstraction Using Contextual Information
## 1 Introduction
However, all of the aforementioned cases highlight the need to bridge the gap between the different levels of granularity at which the data are collected and the expected process activities. Although this problem has received substantial interest in the literature, there is a lack of approaches able to abstract activities while considering other attributes, i.e., the context. In this paper, we present CvAMoS, an event abstraction technique that aims to identify recurring sequences of activities while also considering contextual variables. In particular, the approach works on the sensors that have detected the execution of an activity, as well as environmental sensors that have recorded the state of the environment while the activity was being performed. Our goal is to identify recurring patterns of sensor events and interpret them as higher-level activities that can be represented at the process level. The proposed method is an extension of a previous version [9] that only focused on recognizing common sequences of activities that show a high-level of similarity among traces. The research undertaken aims to tackle the following research questions. **RQ1 (Identification)**: Is the approach capable of identifying meaningful patterns of behavior starting from low-level events? **RQ2 (Context)**: Is the approach able to distinguish patterns based on their execution context? **RQ3 (Filter)**: Is the approach capable of abstracting an event log by aggregating patterns? **RQ4 (Time)**: Is the algorithm applicable, from a computation time point of view? Is the approach capable of abstracting the patterns in terms of time? CvAMoS focuses on the identification of recurring patterns of behavior, at the trace level, in the form of sub-traces that show a high-level of commonalities. The algorithm considers environmental sensors while identifying the patterns, in order to distinguish them based on the state of the environment. The approach generates a list of patterns, which can then be used to abstract the original event log. The objective is therefore to recognize a series of activities that are common in most traces, under the same set of circumstances. The sequences illustrate a specific configuration of sensor measurements that can be linked to the execution of a higher-level activity. To better explain the functionalities of the approach, let us consider the example of a person living in a smart apartment equipped with presence sensors. The sensor system also produces continuous values on the weather conditions. The sensors allow for capturing activities as well as contextual variables such as the weather. Let us consider an environment in which it is snowing, and the person is more likely to go to the window often to admire the view. Let us consider another day when the weather is foggy and rainy, in this case going to the window often could be linked to anomalous behavior (i.e., there is no apparent reason for such action, which is therefore considered anomalous). Having information regarding the context allows distinguishing between these two activities, which can be labeled as _go to the window while it is snowing_ and _go to the window while it is foggy_. The objective of CvAMoS is to recognize a series of activities that are common in the traces, while taking into consideration continuous variables that describe the context. The rest of the paper is structured as follows. Section 2 introduces the related work. The solution is presented in Section 4 that is then evaluated in Section 5 including a discussion of the results, and the limitation of the approach. Section 7 concludes the paper.