The increasing number of IoT devices equipped with sensors and actuators pervading every domain of everyday life allows for improved automated monitoring and analysis of processes executed in IoT-enabled environments. While sophisticated analysis methods exist to detect specific types of activities from low-level IoT data, a general approach for detecting activity executions that are part of more complex business processes does not exist. Moreover, dedicated information systems to orchestrate or monitor process executions are not available in typical IoT environments. As a consequence, the large corpus of existing process analysis and mining techniques to check and improve process executions cannot be applied. In this work, we develop an interactive method guiding the analysis of low-level IoT data with the goal of detecting higher-level process activity executions. The method is derived following the exploratory data analysis of an IoT data set from a smart factory. We propose analysis steps, sensor-actuator-activity patterns, and the novel concept of activity signatures that are applicable in many IoT domains. The method shows to be valuable for the early stages of IoT data analyses to build a ground truth based on domain knowledge and decisions of the process analyst, which can be used for automated activity detection in later stages.

Inter-organizational business processes can be designed top-down: starting from a global process model, where each activity and gateway is assigned to one of the participating organizations, local processes (or views/stubs for refinement) are generated for each organization. The resulting inter-organizational process is executed by the local processes in a fully distributed manner. Implementing the data-flow in such a scenario is highly complex, both because of the combinatorial explosion of possible solutions and because of conflicting goals for implementing the data-flow. We present a flexible, heuristic algorithm for implementing the data-flow for inter-organizational processes according to various user preferences, which significantly improves over the base-line approaches proposed so far. The approach is fully implemented and a comprehensive, multi-dimensional evaluation is provided.