Internet of Thing (IoT) devices enable the collection and exchange of data over the Internet, whereas Business Process Management (BPM) is concerned with the analysis, discovery, implementation, execution, monitoring, and evolution of business processes. By enriching BPM systems with IoT capabilities, data from the real world can be captured and utilized during process execution in order to improve online process monitoring and data-driven decision making. Furthermore, this integration fosters prescriptive process monitoring, e.g., by enabling IoT-driven process adaptions when deviations between the digital process and the one actually happening in the real world occur. As a prerequisite for exploiting these benefits, IoT-related aspects of business processes need to be modeled. To enable the use of sensors, actuators, and other IoT objects in combination with process models, we introduce a BPMN 2.0 extension with IoT-related artifacts and events. We provide a first evaluation of this extension by applying it in two case studies for modeling of IoT-aware processes.

The relevance of the Internet of Things (IoT) for Business Process Management (BPM) support is increasing. IoT devices enable the collection and exchange of data over the Internet, whereby each physical device is uniquely identifiable through its embedded computing system. BPM, in turn, is concerned with analyzing, discovering, modeling, executing, and monitoring (digitized) business processes. By enhancing BPM systems with IoT capabilities, real-world data can be gathered and considered during process execution to enhance process monitoring as well as IoT-driven decision making. In this context, the aggregation of low-level IoT data into high-level process-relevant data constitutes a fundamental step towards IoT-driven decisions in business processes. This paper presents IoT Decision Making for Business Process Model and Notation (IoTDM4BPMN) a webbased framework for modeling, executing, and monitoring IoTdriven decisions in real-time. We give insights into the design and implementation of IoTDM4BPMN and provide a case study as a first validation that applies IoTDM4BPMN to the modeling, executing, and monitoring of a real-world IoT-driven decision process.

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The involvement of the Internet of Things (IoT) in Business Process Management (BPM) solutions is continuously increasing. While BPM enables the modeling, implementation, execution, monitoring, and analysis of business processes, IoT fosters the collection and exchange of data over the Internet. By enriching BPM solutions with real-world IoT data both process automation and process monitoring can be improved. Furthermore, IoT data can be utilized during process execution to realize IoT-driven business rules that consider the state of the physical environment. The aggregation of low-level IoT data into processrelevant, high-level IoT data is a paramount step towards IoT-driven business processes and business rules respectively. In this context, Business Process Modeling and Notation (BPMN) and Decision Model and Notation (DMN) provide support to model, execute, and monitor IoTdriven business rules, but some challenges remain. This paper derives the challenges that emerge when modeling, executing, and monitoring IoT-driven business rules using BPMN 2.0 and DMN standards.

Business process models represent detailed information regarding the documentation, optimization, and automation of organizational processes. With an increasing level of detail, such models become rapidly complex, thus hindering their correct comprehension (i.e., both, the efficiency and accuracy of understanding decrease). Furthermore, once a process model is created, the view on it and the respective level of detail remains rigid for all stakeholders. However, a process model may contain information, which is less relevant for certain stakeholders (e.g., domain experts). Consequently, the comprehension of process models becomes more difficult, as all information in a model needs to be read. Existing modeling systems do not offer the functionality to dynamically change the level of detail of a process model according to the stakeholders' preferences. To address this issue, the work at hand presents the Dynamic Visualization of Process Models (DyVProMo), a lightweight web-based tool that enables the dynamic visualization of information in process models expressed in terms of the Business Process Model and Notation 2.0.