扩展预测流程监控，支持多方协作流程的实时预测与干预。
Extending predictive process monitoring for collaborative processes
- 针对跨组织流程特点，改进传统预测方法
- 可预测下个参与者、下一消息等协作关键信息
- 适合政府、企业协同系统中的流程优化与风险预警
基于业务流程执行数据的过程挖掘主要聚焦于单一组织内部的编排型流程。而协作型（跨组织）流程，如电子政务场景中涉及多个组织，其复杂性更高，实施与发现、预测及分析均面临更多挑战。预测流程监控利用历史执行数据预测当前案例的后续行为，例如预测下一个活动和剩余时间，以提前发现偏差、违规和延迟，从而采取预防措施（如资源重新分配）。本文提出对传统流程预测方法的扩展，适用于协作型流程，引入该类流程特有的信息，例如下一个参与者的活动或两个参与者间将交换的消息，提升预测的准确性和实用性。
原文摘要 · Abstract (English)
Process mining on business process execution data has focused primarily on orchestration-type processes performed in a single organization (intra-organizational). Collaborative (inter-organizational) processes, unlike those of orchestration type, expand several organizations (for example, in e-Government), adding complexity and various challenges both for their implementation and for their discovery, prediction, and analysis of their execution. Predictive process monitoring is based on exploiting execution data from past instances to predict the execution of current cases. It is possible to make predictions on the next activity and remaining time, among others, to anticipate possible deviations, violations, and delays in the processes to take preventive measures (e.g., re-allocation of resources). In this work, we propose an extension for collaborative processes of traditional process prediction, considering particularities of this type of process, which add information of interest in this context, for example, the next activity of which participant or the following message to be exchanged between two participants.
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