Abstract
Event logs reflect the behavior of business processes that are mapped in organizational information systems. Predictive process monitoring (PPM) transforms these data into value by creating process-related predictions that provide the insights required for proactive interventions at process runtime. Existing PPM techniques require sufficient amounts of event data or other relevant resources that might not be readily available, preventing some organizations from utilizing PPM. The transfer learning-based PPM technique presented in this paper allows organizations without suitable event data or other relevant resources to implement PPM for effective decision support. The technique is instantiated in two real-life use cases, based on which numerical experiments are performed using event logs for IT service management processes in an intra- and inter-organizational setting. The results of the experiments suggest that knowledge of one business process can be transferred to a similar business process in the same or a different organization to enable effective PPM in the target context. With the proposed technique, organizations can benefit from transfer learning in an intra- and inter-organizational setting, where resources like pre-trained models are transferred within and across organizational boundaries.
Abstract (translated)
事件日志反映了组织信息系统中映射的业务流程的行为。预测过程监控（PPM）通过将这些数据转化为与实时业务流程相关的预测，从而提供了价值，使组织能够进行主动干预以支持决策。现有的PPM技术通常需要大量的事件数据或其他相关资源，而这些资源在某些情况下可能并不容易获得，导致一些组织无法使用PPM。本文介绍的一种基于迁移学习的PPM技术允许没有合适事件数据或其它相关资源的组织也能实施有效的PPM，以支持其业务决策。 该技术通过两个现实用例得以具体应用，并且在此基础上利用信息技术服务管理过程中的事件日志，在组织内部和跨组织的不同场景下进行了数值实验。这些实验证明了一种业务流程的知识可以被转移到相似的其他业务流程中——无论是同一家公司还是不同的公司，从而在目标上下文中实现有效的PPM。 通过这种提议的技术，无论是在同一组织内部还是跨组织间，资源如预训练模型都可以进行转移利用，从而使各个组织能够从迁移学习中受益。
URL
https://arxiv.org/abs/2508.08061
