The Internet of Things (IoT) has been shown to be very valuable for Business Process Management (BPM), for example, to better track and control process executions. While IoT actuators can automatically trigger actions, IoT sensors can monitor the changes in the environment and the humans involved in the processes. These sensors produce large amounts of discrete and continuous data streams, which hold the key to understanding the quality of the executed processes. However, to enable this understanding, it is needed to have a joint representation of the data generated by the process engine executing the process, and the data generated by the IoT sensors. In this paper, we present an extension of the event log standard format XES called DataStream. DataStream enables the connection of IoT data to process events, preserving the full context required for data analysis, even when scenarios or hardware artifacts are rapidly changing. The DataStream extension is designed based on a set of goals and evaluated by creating two datasets for real-world scenarios from the transportation/logistics and manufacturing domains.

Clinical guidelines support physicians in the evidence-based treatment of patients. The technical verification of guideline compliance is not trivial, since guideline knowledge is usually represented textually and none of the approaches to computer-interpretable guideline representation has yet been able to establish itself. Due to the procedural nature of treatment sequences, this case study examines the applicability of a guideline process model to real hospital data for verification of guideline compliance. For this purpose, the limitations and challenges in the transformation of clinical data into an event log and in the application of conformance checking to align the data with the guideline reference model are investigated. As a data set, we use treatment data of skin tumor patients from a cancer registry enriched by hospital information system data. The results show the difficulty of applying process mining to medically complex and heterogeneous data and the need for complex preprocessing. The variability of clinical processes makes the application of global conformance checking algorithms challenging. In addition, the work shows the semantic weakness of the alignments and the need for new semantically sensitive approaches.

Modern technologies such as the Internet of Things (IoT) are becoming increasingly important in various fields, including business process management (BPM) research. An important area of research in BPM is process mining, which can be used to analyze event logs e.g., to check the conformance of running processes. However, the data ingested in IoT environments often contain data quality issues (DQIs) due to system complexity and sensor heterogeneity, among other factors. To date, however, there has been little work on IoT event logs, DQIs occurring in them, and how to handle them. In this case study, we generate an IoT event log, perform a structured data quality analysis, and describe how we addressed the problems we encountered in pre-processing.