# An Interactive Method for Detection of Process Activity Executions from IoT Data +
## 1 Introduction  
The Internet of Things (IoT) is experiencing increasing adoption in almost every domain of everyday life. Smart, interconnected devices consisting of a multitude of sensors and actuators are able to sense and interact with the physical world and other smart devices. On the one hand, these smart devices are more and more involved in the automation and execution of business processes. On the other hand, they act as interfaces with the physical world to provide real-time data about the execution of processes and activities as well as their surroundings. For this reason, the IoT is receiving increasing attention as an enabler of _smart_ business process management (BPM) [1]. In many IoT domains (e.g., smart homes, smart healthcare and smart manufacturing) processes and routines describing repetitive sequences of activities and interactions among humans, computers and smart devices to achieve a common goal are omnipresent. However, the explicit notion of a _process_ as a first-class citizen is rarely found in IoT. Existing solutions and related research are usually concerned with the tracking of specific types of activities and ad hoc interactions based on events from specialized sensors and analysis algorithms, but not with the overall processes these activities are part of. On the other hand, BPM systems are rarely used in IoT contexts to orchestrate, or at least to monitor, the execution of processes [2]. The goal of this work is to bridge this gap between event-based and process-based systems [1] and to derive a method to detect process activity executions from IoT data without relying on the existence of a BPM system for monitoring. By making processactivities monitorable from IoT data, we are able to then apply the rich corpus of existing process mining techniques to discover (new) processes, check conformance of process executions, and optimize processes [1; 3]. The following _research question_ guides the development of the data analysis method: _Which steps need to be taken to manually analyze IoT data for process activity executions?_ In this work, we focus on the interactive analysis of IoT data performed manually in an early stage by a data analyst. While several unsupervised machine learning approaches have produced unsatisfying results due to the complexity of IoT data and missing domain knowledge to build a ground truth [4], we propose a general, structured method to support the analyst with detecting and deriving knowledge about activity executions step-by-step from IoT data. This knowledge can then be used as ground truth for supervised learning and application to large data sets. This work is structured as follows: Section 2 introduces the relevant background, the goal of our work and a scenario from smart manufacturing. Section 3 discusses related work. Section 4 develops the method for detecting activity executions from IoT data. Section 5 describes the application of the method in a proof of concept evaluation. Section 6 discusses the method. Section 7 concludes the paper and shows starting points for future work. This paper is an extended version of the work presented in [5]. It was modified and extended as follows. The paper focuses on the research question related to the development of the analysis method. The background section provides a detailed discussion of _process-awareness of IoT data_ and _IoT-awareness of process data_ (Section 2.1). The smart manufacturing scenario and example processes are explained in more detail (Section 2.2). Related work is updated and extended (Section 3). The method for activity detection is revised, extended and derived more profoundly (Section 4). Several examples from the scenario are used to illustrate and back up the individual steps. The method now also includes a classification of sensor and actuator patterns related to activity detection (Section 4.4.1). It also discusses the discovery of processes and the existence of ambiguities. The tooling infrastructure and data pipeline are described briefly (Section 4.8).