发表机构
Universidad ORT Uruguay; Universidad de la República（乌拉圭ORT大学; 乌拉圭共和国大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种将协作预测流程监控与以对象为中心的流程挖掘结合的框架，通过语义映射、任务重述及实现的流水线，在五组日志上验证了其有效性。
AI 中文摘要
协作型跨组织流程的预测流程监控（PPM）需要对多个相互依赖的实体进行推理，包括参与者、消息、本地执行和全局协作案例。现有方法通过协作属性扩展传统事件日志，但仍采用单案例视角，使该结构的大部分内容隐含。以对象为中心的流程挖掘（OCPM）提供了一种替代方案，将这些实体表示为具有显式关系和多种案例概念的一流对象。本研究通过三项贡献将协作PPM与OCPM结合：（i）从扩展协作事件日志到符合OCED的以对象为中心表示的形式语义映射，以OCEL 2.0序列化；（ii）将协作预测任务重新表述为以对象为中心的预测任务；（iii）实现所提映射的可复现转换器和预测流水线。我们在四个公共协作事件日志以及从BPI Challenge 2013事件管理日志派生的第五个日志上评估该框架，使用五种预测策略（涵盖表格、序列和图原生编码）执行14项重新表述的任务。我们还讨论了该方法的优势、局限性以及对其有效性的威胁。该表示使协作结构显式化，且能自然地基于以案例为中心分类法之外的对象关系陈述预测目标，代价是增加了关系复杂性并依赖以对象为中心的工具。
英文摘要
Predictive Process Monitoring (PPM) of collaborative, inter-organizational processes requires reasoning over multiple interdependent entities, including participants, messages, local executions, and the global collaboration case. Existing approaches extend traditional event logs with collaboration attributes but retain a single-case perspective, leaving much of this structure implicit. Object-centric process mining (OCPM) provides an alternative by representing these entities as first-class objects with explicit relations and multiple notions of case. This study connects collaborative PPM and OCPM through three contributions: (i) a formal semantic mapping from extended collaborative event logs to an OCED-conformant object-centric representation, serialized in OCEL 2.0; (ii) a reformulation of collaborative prediction tasks as object-centric prediction tasks; and (iii) a reproducible converter and prediction pipeline implementing the proposed mapping. We evaluate the framework on four public collaborative event logs and a fifth derived from the BPI Challenge 2013 incident-management log by executing the fourteen reformulated tasks using five predictive strategies across tabular, sequential, and graph-native encodings. We further discuss the benefits, limitations, and threats to the approach's validity. The representation makes collaboration structure explicit and makes it natural to state prediction targets based on object relations that fall outside the case-centric taxonomy, at the cost of increased relational complexity and dependence on object-centric tooling.
CommentsSubmitted to Information Systems, under review
