Mixed-paradigm process models integrate strengths of procedural and declarative representations like Petri nets and Declare. They are specifically interesting for process mining because they allow capturing complex behaviour in a compact way. A key research challenge for the proliferation of mixed-paradigm models for process mining is the lack of corresponding conformance checking techniques. In this paper, we address this problem by devising the first approach that works with intertwined state spaces of mixed-paradigm models. More specifically, our approach uses an alignment-based replay to explore the state space and compute trace fitness in a procedural way. In every state, the declarative constraints are separately updated, such that violations disable the corresponding activities. Our technique provides for an efficient replay towards an optimal alignment by respecting all orthogonal Declare constraints. We have implemented our technique in ProM and demonstrate its performance in an evaluation with real-world event logs.

混合范例过程模型整合了过程和声明式表示的优势，例如Petri网和Declare。它们对于流程挖掘特别有趣，因为它们允许以紧凑的方式捕获复杂的行为。流程挖掘的混合范例模型的扩散所面临的主要研究挑战是缺乏相应的一致性检查技术。在本文中，我们通过设计第一种方法来解决这个问题，该方法适用于混合范式模型的交织状态空间。更具体地说，我们的方法使用基于对齐的重播来探索状态空间并以过程方式计算跟踪适用性。在每种状态下，声明性约束都会分别更新，这样，违反行为将禁用相应的活动。通过尊重所有正交声明，我们的技术可向最佳对齐方式高效回放约束。我们已经在ProM中实施了我们的技术，并通过真实事件日志评估了其性能。