Abstract 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.
In addition to contractual definitions, monitoring in decentralized process execution needs to be defined during choreography modeling. provide an extension to the standard BPMN 2.0 monitoring injection points to support choreographies @cite_11 . also propose a technology-driven approach to enable choreography monitoring by extending BPEL4Chor with an event-oriented monitoring agreement @cite_33 . To address privacy concerns of process participants, events can only be defined based on a publicly available process model. How each participant maps public choreography activities to internal processes remains hidden. None of the approaches discussed so far makes use of a decentralized ledger to store and distribute monitoring data in a trustworthy way. Notably, if this is supported at all, it is necessary to explicitly define which data is shared with whom.
@cite_33 @cite_11
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Business process monitoring in the area of service oriented computing is typically performed using business activity monitoring technology in an intra-organizational setting. Due to outsourcing and the increasing need for companies to work together to meet their joint customer demands, there is a need for monitoring of business processes across organizational boundaries. Thereby, partners in a choreography have to exchange monitoring data, in order to enable process tracking and evaluation of process metrics. In this paper, we describe an event-based monitoring approach based on BPEL4Chor service choreography descriptions. We show how to define monitoring agreements specifying events each partner in the choreography has to provide. We distinguish between resource events and complex events for calculation of process metrics using complex event processing technology. We present our implementation and evaluate the concepts based on a scenario. Cross-organizational business processes gain more and more attention in the scientific community. For modeling these processes, choreographies are an adequate solution to describe the observable behavior for each business participant and the corresponding protocols between them. Nevertheless, current approaches mainly focus on modeling and conformance checking before runtime. In contrast, this contribution concentrates on the runtime validation of choreographies and will thus propose a monitoring infrastructure that supports this validation. We will sketch the requirements for a monitoring infrastructure as well as the solutions in order to meet these requirements. Without such a reliable monitoring framework, a runtime validation framework simply would not be able to correctly validate the interactions. Software-as-a-service (SaaS) providers are further expanding their offering by growing into the space of business process outsourcing (BPO). Therefore, the SaaS provider wants to administer and manage the business process steps according to a service level agreement. Outsourcing of business processes results in decentralized business workflows. However, current business process modeling languages, e.g. Business Process Execution Language (BPEL), Business Process Model and Notation (BPMN), are based highly on a centralized execution model and current BPMN engines offer limited constructs for federation and decentralized execution. To guarantee execution of business processes according to a service level agreement, different parties involved in a federated workflow must be able to inspect the state of external workflows. This requires advanced inspection interfaces and monitoring facilities. Current business process modeling languages must thus be extended to support monitoring in the s pecification, support modeling and support deployment of decentralized workflows. In this paper, correlation and monitoring extensions for BPMN are described. These extensions to BPMN are described such that the existing specification can still be used as is in a backwards compatible way. This paper presents the application of a Bayesian nonparametric time-series model to process monitoring and fault classification for industrial robotic tasks. By means of an alignment task performed with a real robot, we show how the proposed approach allows to learn a set of sensor signature models encoding the spatial and temporal correlations among wrench measurements recorded during a number of successful task executions. Using these models, it is possible to detect continuously and on-line deviations from the expected sensor readings. Separate models are learned for a set of possible error scenarios involving a human modifying the workspace configuration. These non-nominal task executions are correctly detected and classified with an on-line algorithm, which opens the possibility for the development of error-specific recovery strategies. Our work is complementary to previous approaches in robotics, where process monitors based on probabilistic models, but limited to contact events, were developed for control purposes. Instead, in this paper we focus on capturing dynamic models of sensor signatures throughout the whole task, therefore allowing continuous monitoring and extending the system ability to interpret and react to errors.