Detecting undesired process behavior is one of the main tasks of process mining and various conformance-checking techniques have been developed to this end. These techniques typically require a normative process model as input, specifically designed for the processes to be analyzed. Such models are rarely available, though, and their creation involves considerable manual effort.However, reference process models serve as best-practice templates for organizational processes in a plethora of domains, containing valuable knowledge about general behavioral relations in well-engineered processes. These general models can thus mitigate the need for dedicated models by providing a basis to check for undesired behavior. Still, finding a perfectly matching reference model for a real-life event log is unrealistic because organizational needs can vary, despite similarities in process execution. Furthermore, event logs may encompass behavior related to different reference models, making traditional conformance checking impractical as it requires aligning process executions to individual models. To still use reference models for conformance checking, we propose a framework for mining declarative best-practice constraints from a reference model collection, automatically selecting constraints that are relevant for a given event log, and checking for best-practice violations. We demonstrate the capability of our framework to detect best-practice violations through an evaluation based on real-world process model collections and event logs.

翻译：检测非期望的流程行为是流程挖掘的主要任务之一，为此已开发出多种合规性检查技术。这些技术通常需要专门针对待分析流程设计的规范性流程模型作为输入。然而，此类模型往往难以获取，且其创建需要大量人工投入。参考流程模型作为众多领域组织流程的最佳实践模板，蕴含着关于良好设计流程中通用行为关系的宝贵知识。因此，这些通用模型可通过提供检测非期望行为的基础，缓解对专用模型的需求。尽管如此，为现实事件日志寻找完全匹配的参考模型并不现实，因为尽管流程执行存在相似性，组织需求可能存在差异。此外，事件日志可能包含与不同参考模型相关的行为，这使得传统合规性检查方法因需将流程执行与单个模型对齐而难以实施。为利用参考模型进行合规性检查，我们提出一个框架：从参考模型集合中挖掘声明式最佳实践约束，自动筛选与给定事件日志相关的约束，并检查最佳实践违规情况。我们通过基于真实世界流程模型集合与事件日志的评估，验证了该框架检测最佳实践违规的能力。