Abstract Locally distributed processes include several process participants working on tasks at different locations, e.g., craftspeople working on construction sites. Compared to classical IT environments, new challenges emerge due to the spatial context of a process. Real-time location data from Internet of Things (IoT) devices can help businesses implement more efficient and effective processes through business process management (BPM). However, only small parts of existing research have touched on those advantages, while the architecture and implementation of actual executable location-aware processes area has only been vaguely considered. Therefore, we introduce and present a non-exhaustive list of patterns for using location data in BPM while also including an actual implementation of a location-aware approach using a multilayer system architecture based on standard BPM technology. These can be used to leverage the location perspective of process entities as contextual data in BPM.

Abstract Declarative process modelling languages focus on describing a process by restrictions over the behaviour, which must be satisfied throughout the whole process execution. Hence, they are well suited for modelling knowledge-intensive processes with many decision points. However, such models can be hard to read and understand, which affect the modelling and maintenance of the process models tremendously as well as their execution. When executing such declarative (multi-perspective) process models, it may happen that the execution of activities or the change of data values may result in the non-executability of crucial activities. Hence, it would be beneficial to know all consequences of decisions to give recommendations to the process participants. A look-ahead attempts to predict the effects of executing an activity towards possible consequences within an a priori defined time window. The prediction is based on the current state of the process execution, the intended next event and the underlying process model. While execution engines for single-perspective imperative process models already implement such functionality, execution approaches, for multi-perspective declarative process models that involve constraints on data and resources, are less mature. In this paper, we introduce a simulation-based look-ahead approach for multi-perspective declarative process models. This approach transforms the problem of a context-aware process simulation into a SAT problem, by translating a declarative multi-perspective process model and the current state of a process execution into a specification of the logic language Alloy. Via a SAT solver, process trajectories are generated that either satisfy or violate this specification. The simulated process trajectories are used to derive consequences and effects of certain decisions at any time of process execution. We evaluate our approach by means of three examples and give some advice for further optimizations.

Abstract Automated process discovery is a technique that extracts models of executed processes from event logs. Logs typically include information about the activities performed, their timestamps and the resources that were involved in their execution. Recent approaches to process discovery put a special emphasis on (human) resources, aiming at constructing resource-aware process models that contain the inferred resource assignment constraints. Such constraints can be complex and process discovery approaches so far have missed the opportunity to represent expressive resource assignments graphically together with process models. A subsequent verification of the extracted resource-aware process models is required in order to check the proper utilisation of resources according to the resource assignments. So far, research on discovering resource-aware process models has assumed that models can be put into operation without modification and checking. Integrating resource mining and resource-aware process model verification faces the challenge that different types of resource assignment languages are used for each task. In this paper, we present an integrated solution that comprises (i) a resource mining technique that builds upon a highly expressive graphical notation for defining resource assignments; and (ii) automated model-checking support to validate the discovered resource-aware process models. All the concepts reported in this paper have been implemented and evaluated in terms of feasibility and performance.

Abstract 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.

The spectrum of an organization’s business processes ranges from routine processes with a well-defined flow to agile processes with a degree of uncertainty. The Process Navigation platform aims at supporting both types of processes as well as combinations of them. It offers execution support for tra

Recent work shows an increasing interest of the Business Process Management (BPM) community in unstructured, socalled "human-centric" processes. Case Management (CM) is a new trend that focuses on the support of collaborative human-centric processes. Although CM provides concepts that support human-

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The declarative modeling approach promises to be a suitable means for the description of rather unforeseen and less rigid business processes. However, today’s approaches for the execution of such declarative processes lack certain essential capabilities. One of those is traceability which means that