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 events and multiple objects, leading to the discovery of more detailed and realistic yet complex process models. The lack of methods to adjust the granularity of the analysis prevents users from leveraging 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 analysts to change 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, covering a four-year period and approximately 400 students, as a case of object-centric educational process mining. This case study shows significant improvements in the precision and fitness of the discovered models after applying the operations. In addition, we evaluate the scalability of the operators on large, publicly available OCELs derived from the Business Process Intelligence Challenge datasets, demonstrating that the operations remain computationally feasible on industrial-scale event logs. This approach can empower analysts to perform more flexible and comprehensive process exploration, unlocking actionable insights through flexible granularity adjustments.

翻译：以不同粒度级别分析过程数据对于获取可操作洞察并支持明智决策至关重要。面向对象的事件数据通过捕捉事件与多个对象之间的交互，增强了过程挖掘能力，从而发现更详细、更真实但更为复杂的过程模型。由于缺乏调整分析粒度的方法，用户难以充分发挥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、展开与折叠，使分析人员能够在处理面向对象事件日志时改变分析粒度。这些操作允许分析人员在详细过程模型与聚合过程模型之间无缝切换，有助于发现需要不同抽象层级的洞察。我们对这些操作进行了形式化定义，并在开源Python库中予以实现。为验证其实用性，我们将该方法应用于从学习管理系统提取的真实世界OCEL数据（涵盖四年时间及约400名学生），以此作为面向对象教育过程挖掘的案例。该案例研究表明，应用这些操作后，所发现模型的精确度与拟合度均有显著提升。此外，我们基于商业过程智能挑战数据集中的大型公开OCEL评估了操作的可扩展性，证明这些操作在工业规模的事件日志上仍保持计算可行性。该方法能够助力分析人员开展更灵活、更全面的过程探索，通过灵活的粒度调整解锁可操作洞察。