Enabling process changes constitutes a major challenge for any process-aware information system. This not only holds for processes running within a single enterprise, but also for collaborative scenarios involving distributed and autonomous partners. In particular, if one partner adapts its private process, the change might affect the processes of the other partners as well. Accordingly, it might have to be propagated to concerned partners in a transitive way. A fundamental challenge in this context is to find ways of propagating the changes in a decentralized manner. Existing approaches are limited with respect to the change operations considered as well as their dependency on a particular process specification language. This paper presents a generic change propagation approach that is based on the Refined Process Structure Tree, i.e., the approach is independent of a specific process specification language. Further, it considers a comprehensive set of change patterns. For all these change patterns, it is shown that the provided change propagation algorithms preserve consistency and compatibility of the process choreography. Finally, a proof-of-concept prototype of a change propagation framework for process choreographies is presented. Overall, comprehensive change support in process choreographies will foster the implementation and operational support of agile collaborative process scenarios.

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The propagation and management of changes in process choreographies has been recently addressed as crucial challenge by several approaches. A change rarely confines itself to a single change, but triggers other changes in different partner processes. Specifically, it has been stated that with an increasing number of partner processes, the risk for transitive propagations and costly negotiations increases as well. In this context, utilizing past change events to learn and analyze the propagation behavior over process choreographies will help avoiding significant costs related to unsuccessful propagations and negotiation failures, of further change requests. This paper aims at the posteriori analysis of change requests in process choreographies by the provision of mining algorithms based on change logs. In particular, a novel implementation of the memetic mining algorithm for change logs, with the appropriate heuristics is presented. The results of the memetic mining algorithm are compared with the results of the actual propagation of the analyzed change events.

Business process collaborations among multiple partners require particular considerations regarding flexibility and change management. Indeed, each change or process redesign originated by a partner may cause ripple effects on other partners participating in the choreography. Consequently, a change request could spread over partners in an unexpected way with relevant costs due to its transitivity (e.g. in supply chains). In order to avoid costly negotiations or propagation failures, understanding this behavior becomes critical. This paper focuses on analyzing the behavior of change requests in process choreographies, i.e. the change propagation behavior. The input data might be available in two different formats, i.e. as change logs or change propagation logs (CPs). In order to understand the data and to explore potential analysis models and techniques, we employ exploratory data analysis as well as analysis techniques from process mining and change management to simulation data. The results yield the requirements for designing a mining algorithm that derives the propagation behavior behind change logs. This algorithm is a memetic algorithm that is based on different heuristics. Its feasibility is shown based on a comparison with the other mining techniques.