Generally, a collaborative business process is a distributed process, in which a set of parallel business processes are involved. These business processes have complementary competencies and knowledge, and cooperate with each other to achieve their common business goals. To ensure the correctness of collaborative business processes, we propose a novel plan-based correctness enforcement approach in this article, which is privacy-preserving, available and efficient. This approach first requires participating organizations to define their business processes. Then, each participating organization employs a set of reduction rules to build the public process of its business process, in which all internal private activities and the flows formed by them are removed. Next, a set of correct plans is generated from these public processes. A plan is essentially a process fragment without alternative routings. From the external perspective (i.e., ignoring all internal private activities and the flows formed by them), a parallel execution of the business processes corresponding to these public processes follows only one such plan. Lastly, each participating organization independently refactors its business process using these resulting correct plans. Using the message places (corresponding to the actual communication interfaces), these refactored processes are composed in parallel. Thus, a correct and loosely coupled enforced process is constructed. This approach is evaluated on actual collaborative business processes, and the experimental results show that compared with state-of-the-art enforcement proposals, it can achieve correctness enforcement while protecting the business privacy of organizations and is available. Meanwhile, it is also more efficient and scalable, even a collaborative business process with tens of millions of states can be enforced within a few seconds.

When an emergency occurs, disposal needs to be built to reduce the risk imposed on life, property, and environment. Generally, the disposal is organized as a cross-organization emergency response process (CERP). To achieve better-emergency response services, its correctness analysis is an important task that needs to be dealt with at design time. In this article, we propose a novel correctness analysis approach for CERPs. Given a CERP, this approach first decomposes it into a set of instance nets. Then, it excludes the invalid instance nets (each corresponds to an incomplete process instance) and adopts the stubborn set to check the structural correctness of each valid instance net without considering resource factors, as well as introduces a structure-based resource analysis (SRA) method to determine whether the resources in it are sufficient. Finally, it determines the correctness of the CERP by comparing the numbers of the correct instance nets and all valid instance nets. If the CERP is incorrect, it returns the correct instance nets, which capture the parts of the CERP that can be executed correctly. This approach is evaluated on an actual data set, and the comparison results show that it outperforms the state-of-the-art technique in terms of effectiveness and efficiency.

Since the data-aware business process usually covers the control flow and data flow, and both of them have a direct impact on its execution, it is challenging to ensure its correctness. In this article, we propose a novel correctness enforcement approach for the data-aware business the processes. Given a data-aware business process, this approach first relies on the notion of the execution paths to capture all the parts of it that can be executed correctly. More specifically, it first splits the data-aware business process into a set of the execution paths, and then presents the notion of well-formedness to determine whether each execution path is correct. Based on these captured correct execution paths, an execution path-oriented strategy is generated, which is nonintrusive, and can enforce it to follow one of its correct execution paths during each execution, thereby realizing its correct execution. This approach is evaluated using the extensive experiments, which shows that it is effective and efficient, as well as scalable in practice.