Numerous process discovery techniques exist for generating process models that describe recorded executions of business processes. The models are meant to generalize executions into human-understandable modeling patterns, notably parallelism, and enable rigorous analysis of process deviations. However, well-defined models with parallelism returned by existing techniques are often too complex or generalize the recorded behavior too strongly to be trusted in a practical business context. We bridge this gap by introducing the Probabilistic Inductive Miner (PIM) based on the Inductive Miner framework. PIM compares in each step the most probable operators and structures based on frequency information in the data, which results in block-structured models with significantly higher accuracy. All design choices in PIM are based on business context requirements obtained through a user study with industrial process mining experts. PIM is evaluated quantitatively and in an novel kind of empirical study comparing users' trust in discovered model structures. The evaluations show that PIM strikes a unique trade-off between model accuracy and model complexity, that is conclusively preferred by users over all state-of-the-art process discovery methods.

中文翻译：

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使用概率感应挖掘器在准确性和简单性方面取得新的平衡

存在许多流程发现技术来生成描述记录的业务流程执行的流程模型。这些模型旨在将执行概括为人类可理解的建模模式，尤其是并行性，并能够对流程偏差进行严格分析。然而，由现有技术返回的具有并行性的定义明确的模型通常过于复杂，或者将记录的行为概括得太强，以至于在实际业务环境中不可信。我们通过引入基于 Inductive Miner 框架的 Probabilistic Inductive Miner (PIM) 来弥合这一差距。PIM 在每个步骤中根据数据中的频率信息比较最可能的算子和结构，从而产生具有更高准确度的块结构模型。PIM 中的所有设计选择都基于通过与工业过程挖掘专家进行的用户研究获得的业务环境要求。PIM 被定量评估，并在一种新型的实证研究中比较用户对发现的模型结构的信任。评估表明，PIM 在模型准确性和模型复杂性之间取得了独特的权衡，最终用户更喜欢所有最先进的过程发现方法。