# DataStream XES Extension: Embedding IoT Sensor Data into Extensible Event Stream Logs
## 2 Foundations and Related Work
### BPM and IoT  
Often, existing work on IoT does not cover the integration of BPM concepts. However, various approaches from the BPM community investigating aspects regarding the integration of BPM with IoT exist and are discussed in different, recent surveys [16; 17; 18]. In the following, we present related work that deals with the integration of BPM in smart IIoT environments. [PERSON] et al. [16] present an overview of cloud BPM systems for (mobile) IoT devices. Process (execution) engines (PEs) are used as part of a middleware to control IoT devices at the edge. [PERSON] et al. [19] discuss the integration of complex events from IoT with  
Figure 1: Related Work. business processes in the context of smart logistics. [PERSON] et al. [20] focus on this integration of sensor events from industrial production machines and their processing in a BPM context. [PERSON] et al. [21] deal mainly with data exchange and communication between IoT devices and execution engines, namely the aspect of abstraction and encapsulation of sensor events. [PERSON] et al. [22] propose an architecture for IoT-enabled dynamic planning in the domain of smart logistics with an applications layer that serves the role of a PE executing business logic. In a literature study, [PERSON] et al. [23] investigate which approaches, frameworks, and tools are available to integrate business processes and complex events in the logistic domain. [PERSON] et al. [24; 25] present the _PROIEUS_ system to execute self-adaptive cyber-physical workflows in IoT base smart home environments. They assume the existence of services to control IoT devices and collect data for event processing. In more recent work [26], they introduce a method for detecting process activity executions. The approach is based on sensor-actuator-activity patterns and activity signatures. In this context, they use a service-oriented architecture based on [2; 27] to control a physical smart factory by a PE. _SmartPM_[28], on the other hand, supports adaptive processes based on AI planning in situations in which an adaptation of the current process instance is required. Based on this, [PERSON] et al. [29] present how control loops can be applied for process adaptation and recovery by using AI planning and semantics. Very similar to that is the approach by [PERSON] et al. [30] in which they present an architecture for asset administration shell-based business processes. In this context, capabilities of resources are encapsulated in such a way that they can be executed in a PE. The architecture of the _SitOPT_ system for situation-aware adaptive workflows in manufacturing is presented in [31]. It allows recognizing complex failure situations based on rules, leading to the execution of process variations to deal with the detected failures. [PERSON] [32] discusses an end-to-end printing process, relying on a PE on top of a middleware layer encapsulating the hardware and software. This work has been the basis for the _HORSE_ framework [33], which deals mostly with logistics use-cases. [PERSON] et al. [34] propose an 8-layer architecture to cover concerns reaching from low-level machine interactions up to decision-making by domain experts. Notably, they suggest a dedicated _Data Analytics_ layer for sensor data processing. [PERSON] et al. [35] present the _BPMNE4 IoT_ framework, which deals with the aspect that the current BPMN 2.0 standard has not been intended to fully meet the IoT characteristics and thus, is not well-suited in every IoT application scenario. For this purpose, they present a framework that supports modeling, executing, and monitoring IoT-driven processes. [PERSON] et al. [36] analyzes IoT frameworks and ontologies that can be utilized to extend the current BPMN representation standard. In addition, they present an automated process for developing digital twins based on actual business processes.