Analyzing process data at varying levels of granularity is important to derive actionable insights and support informed decision-making. Object-Centric Event Data (OCED) enhances process mining by capturing interactions among multiple objects within events, leading to the discovery of more detailed and realistic yet complex process models. The lack of methods to adjust the granularity of the analysis limits users to leverage the full potential of Object-Centric Process Mining (OCPM). To address this gap, we propose four operations: drill-down, roll-up, unfold, and fold, which enable changing the granularity of analysis when working with Object-Centric Event Logs (OCEL). These operations allow analysts to seamlessly transition between detailed and aggregated process models, facilitating the discovery of insights that require varying levels of abstraction. We formally define these operations and implement them in an open-source Python library. To validate their utility, we applied the approach to real-world OCEL data extracted from a learning management system that covered a four-year period and approximately 400 students. Our evaluation demonstrates significant improvements in precision and fitness metrics for models discovered before and after applying these operations. This approach can empower analysts to perform more flexible and comprehensive process exploration, unlocking actionable insights through adaptable granularity adjustments.

翻译：在不同粒度层次上分析过程数据对于获取可操作的见解和支持明智决策至关重要。面向对象的事件数据通过捕获事件中多个对象间的交互，增强了过程挖掘能力，从而能够发现更详细、更真实但更复杂的过程模型。缺乏调整分析粒度的方法限制了用户充分利用面向对象过程挖掘的全部潜力。为弥补这一不足，我们提出了四种操作：下钻、上卷、展开和折叠，这些操作能够在处理面向对象事件日志时改变分析粒度。这些操作使分析人员能够在详细和聚合的过程模型之间无缝切换，便于发现需要不同抽象层次的见解。我们正式定义了这些操作，并在一个开源Python库中实现了它们。为验证其实用性，我们将该方法应用于从一个学习管理系统中提取的真实世界面向对象事件日志数据，该数据覆盖四年时间并涉及约400名学生。我们的评估表明，在应用这些操作前后所发现模型的精确度和适应度指标均有显著提升。该方法能够使分析人员执行更灵活、更全面的过程探索，通过可适应的粒度调整来解锁可操作的见解。