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Imperative languages like BPMN are eminently suitable for representing routine processes and are likewise cumbersome in case of flexible processes. The latter are easier to describe using declarative process modeling languages (DPMLs). However, understandability and tool support of DPMLs are comparatively poor. Additionally, there may be an affinity to a particular language caused by company guidelines or individual preferences. Hence, a technique for automatically translating process models between different languages is required. Process models usually describe several aspects of a process, such as activity orderings and role assignments. Therefore, our approach focuses on translating resourceaware process models. We utilize well-established techniques for process simulation and mining to avoid the definition of cumbersome model transformation rules. Since there is currently no simulation approach for resource-aware declarative process models we additionally describe a solution based on a well-known and expressive logic. The whole translation approach is discussed and evaluated at the example of BPMN and DPIL.

Business processes are frequently executed within application systems that involve humans, computer systems as well as objects of the Internet of Things (IoT). Nevertheless, the usage of IoT technology for system supported process execution is still constrained by the absence of a common system architecture that manages the communication between both worlds. In this paper, we introduce an integrated approach for IoT-aware business process execution that exploits IoT for BPM by providing IoT data in a process-compatible way, providing an IoT data provenance framework, considering IoT data for interaction in a pre-defined process model, and providing wearable user interfaces with context-specific IoT data provision. The approach has been implemented on top of contemporary BPM modeling concepts and system technology. The introduced technique has evaluated extensively in different use cases in industry.

We simulate the two Coupled Model Intercomparison Project scenarios RCP4.5 and RCP8.5, to assess the effects of melt‐induced fresh water on the Atlantic meridional overturning circulation (AMOC). We use a newly developed climate model with high resolution at the coasts, resolving the complex ocean dynamics. Our results show an AMOC recovery in simulations run with and without an included ice sheet model. We find that the ice sheet adds a strong decadal variability on the freshwater release, resulting in intervals in which it reduces the surface runoff by high accumulation rates. This compensating effect is missing in climate models without dynamic ice sheets. Therefore, we argue to assess those freshwater hosing experiments critically, which aim to parameterize Greenland's freshwater release. We assume the increasing net evaporation over the Atlantic and the resulting increase in ocean salinity, to be the main driver of the AMOC recovery.