# An Interactive Method for Detection of Process Activity Executions from IoT Data +
## 3 Related Work
### Process Event Extraction and Abstraction from IoT Data  
Process event extraction comprises the identification of event data relevant for process mining and its extraction from different data sources, e.g., databases and information systems [34; 43]. Recent research is also considering the IoT as new source for event data [12; 44; 45; 46; 47]. In our work, we focus on low-level IoT sensors and actuators serving as the only sources that provide data about the processes and activities being executed in the corresponding smart space.  
Event abstraction aims at bridging the gap in the level of detail at which data is recorded and at which it is analyzed [1; 34; 48]. Especially IoT sensors are capable of producing data at a very fine-grained level [49], which may not be suitable for process mining. Related work proposes to use, e.g., Complex Event Processing (CEP) [2; 50], clustering [51], supervised machine learning [4; 51; 52] or combinations together with expert knowledge [53] to bridge this abstraction gap [54]. To enable process mining, the resulting coarse-grained events then need to be matched to the activities in a process model and correlated with the process instances through event-to-activity mappings [50; 55; 56].  
In [46], the authors present a framework for deriving process event logs from sensor data including the aggregations needed for event abstraction, activity discovery, and event correlation. The lack of process-related information in IoT data often leads to uncertaintieswhen being correlated with specific activities and cases as data patterns can often be associated with multiple candidates [46]. [PERSON] et al. propose a knowledge-driven approach applying interaction mining to transform historical sensor data into process event logs [57]. [PERSON] et al. discuss a supervised method for event abstraction based on activity patterns, which are derived from low-level events using domain knowledge and then aligned with process cases into an event log [58].  
In summary, most approaches assume certain amounts of process-related knowledge when discovering activities and processes from sensor data to generate IoT-enriched event logs [14; 33; 59]. In many cases, existing activity labels have to be connected to the raw events [34; 47; 60]. Related approaches are often not applicable to IoT data sets in more unsupervised settings when almost no process knowledge and limited domain and topology knowledge are given. The goal of our work is to support the process analyst with analyzing unknown IoT data sets for activity and process executions in an early analysis stage. In providing the process analyst with a guideline in the form of a structured interactive analysis method, we rely on domain knowledge for the activities of event extraction, event abstraction and event correlation. Once an initial set of IoT data from a specific IoT domain was analyzed and labeled by the process analyst, the knowledge about IoT data-activity-process correlations can be used as input for more sophisticated supervised learning techniques to automatically analyze larger data sets [37; 53; 57; 58].