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.

Sustainability is an increasingly important area to understand, both for the European general public and politicians. While sustainability entails many directly measurable aspects regarding resource saving and pollution avoidance, the potential contribution of Business Process Management Technology (BPMT) is more indirect. BPMT can contribute by tackling three key areas: Adaptability, Modularity and Transparency. The key idea is to foster sustainability, by allowing companies to (1) react faster and (2) be more flexible through (3) better data availability. BPTM can help improve Adaptability by allowing companies to evolve processes on the fly, and thus reduce lead-time, facilitate first-time-right production, and improve the reliability of arbitrary production processes. Improving Modularity through the utilization of BPMT, i.e., the explicit management of business logic through graphical and easy to adapt process models, is intended to improve resource availability including machines, tools, humans and raw materials, as well as reduce scrap rate and rework ratio. The basis for all of these improvements is of course highly domain-specific data analysis. Here BPTM can foster sustainability by providing contextualized data from process execution. We think BPTM contributes to sustainability goals by letting Domain Experts change and optimize production processes directly, thus reducing the dependency on programmers, data engineers, data scientists and complex interactions required between these stakeholders.