Discovery plays a key role in data-driven analysis of business processes. The vast majority of contemporary discovery algorithms aims at the identification of control-flow constructs. The increase in data richness, however, enables discovery that incorporates the context of process execution beyond the control-flow perspective. A “control-flow first” approach, where context data serves for refinement and annotation, is limited and fails to detect fundamental changes in the control-flow that depend on context data. In this work, we thus propose a novel approach for combining the control-flow and data perspectives under a single roof by extending inductive process discovery. Our approach provides criteria under which context data, handled through unsupervised learning, take priority over control-flow in guiding process discovery. The resulting model is a process tree, in which some operators carry data semantics instead of control-flow semantics. We show that the proposed approach produces trees that are context consistent, deterministic, complete, and can be explainable without a major quality reduction. We evaluate the approach using synthetic and real-world datasets, showing that the resulting models are superior to state-of-the-art discovery methods in terms of measures based on multi-perspective alignments.

发现在业务流程的数据驱动分析中起着关键作用。绝大多数当代发现算法都旨在识别控制流构造。但是，数据丰富性的增加使发现能够在控制流视角之外并入流程执行的上下文。上下文数据用于细化和注释的“控制流优先”方法受到限制，无法检测依赖于上下文数据的控制流的根本变化。因此，在这项工作中，我们提出了一种新颖的方法，通过扩展归纳过程发现，可以将单个控制下的控制流和数据透视图结合起来。我们的方法提供了标准，在这些标准下，通过无监督学习处理的上下文数据在指导过程发现中优先于控制流。结果模型是一个过程树，其中一些运算符携带数据语义而不是控制流语义。我们表明，所提出的方法所产生的树是上下文一致的，确定性的，完整的，并且可以在不降低质量的情况下得到解释。我们使用合成的和真实的数据集评估了该方法，表明在基于多视角比对的度量方面，所得模型优于最新发现方法。