The usage of process choreographies and decentralized Business Process Management Systems has been named as an alternative to centralized business process orchestration. In choreographies, control over a process instance is shared between independent parties, and no party has full control or knowledge during process runtime. Nevertheless, it is necessary to monitor and verify process instances during runtime for purposes of documentation, accounting, or compensation.To achieve business process runtime verification, this work explores the suitability of the Bitcoin blockchain to create a novel solution for choreographies. The resulting approach is realized in a fully-functional software prototype. This software solution is evaluated in a qualitative comparison. Findings show that our blockchain-based approach enables a seamless execution monitoring and verification of choreographies, while at the same time preserving anonymity and independence of the process participants. Furthermore, the prototype is evaluated in a performance analysis.

Automation systems engineering projects typically rely on the collaboration of experts from different engineering disciplines. Multi-disciplinary interaction requires from project participants to exchange (intermediate) artefacts represented and accessed differently in each of the tools. However, local data representations are only fully understood by the tool's expert and therefore not suitable for all possible (human) readers. In order to share engineering knowledge, the process relies on time-consuming human search and translation activities between corresponding tool data representations which causes overhead in communication and is prone to errors.In this paper, the XLink navigation concept is introduced which enables semi-automated translation and navigation between related data elements of different local representations. Using common concepts shared between various engineering tools, XLink enables project participant to define references to local data elements which may be shared with any other project participant. The concept enables direct navigation between related data elements of different local data models without the need for human search and translation activities.Based on a real-world use case and an implemented prototype evaluated in a controlled experiment, we show that the concept is more effective and efficient than the standard human-based approach.