In business process collaboration (BPC), especially when it comes to message communication and data exchange, there are complex data dependencies among sender process, receiver process and messages. However, each participant of the overall BPC develops its part independently as a service, including its own communication part and data flow. As a result, data flow errors across processes may occur easily. In this paper, we propose a method based on BPMN to detect these errors caused by data dependency violations. Our method is inspired by the study of detecting data flow errors within a single process and focuses on a subset of the elements of the BPC model, without having to consider the complete set. In particular, we define a set of data flow error patterns by analyzing and formalizing data dependencies in order to clearly clarify and identify errors. Then we give the corresponding automatic detection algorithm. Finally, through two evaluations, we demonstrate the effectiveness of our proposal. INDEX TERMS Business process management, business process collaboration, data flow error across processes, data dependency.
AI-based process model analysis has attracted more and more interest. Model quality is crucial for such research. At present, inter-organizational business process (IOBP) has been widely used in the model design and development of the distributed system. Before implementing the intelligent analysis of the IOBP model, conformance as a foundation for model quality checking plays a key role because it ensures in advance that the participants can successfully interact without violating the global communication constraints imposed by the choreography. In fact, the multi-instance participant is a common requirement in IOBP. This paper provides a formal approach and framework supporting the conformance between BPMN choreography and collaboration while considering multi-instance participants and message communication modes. As a core, the formalization proposed is based on BNF syntax and structured CSP# processes. It can well support multi-instance features and multiple communication modes. Combined with CSP#, the formal definitions of communication modes and verification properties are given. On this basis, an integrated framework is provided to support automated formal verification referring to multiple communication modes. Finally, a set of experiments is conducted to demonstrate the effectiveness of the proposal.
Crowdsourcing has become a new distributed paradigm, which uses online crowds to solve complex problems. Recently, in order to reduce the development workload and research threshold of crowdsourcing applications, crowdsourcing process modeling is attracting more and more attention. However, complex crowdsourcing processes used for creative and open-ended work have remained out of reach for process modeling, because this type of process usually has a dynamic execution, in which the type, number, and order of tasks and subtasks are often unknown in advance but are determined dynamically at runtime. In this paper, we propose a modeling approach and supporting framework to fill this gap. Specifically, we provide a task model composition to allow task creation on demand, while collaborating on tasks in a tree structure to adapt to the dynamic execution. Moreover, we introduce a set of message communication modes to support data exchange among tasks. Finally, we construct a framework named CrowdModeller to embody this approach. Through two evaluations, we demonstrate its effectiveness.
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