基于时序逻辑模式的面向结果的处方式流程监控
Outcome-Oriented Prescriptive Process Monitoring Based on Temporal Logic Patterns
AI总结：
本文提出一种基于LTLf时序模式的面向结果处方式流程监控系统，通过机器学习学习时序关系与结果的映射，在运行时推荐灵活且可靠的干预模式，并在22个真实事件日志基准上评估。
摘要：
处方式流程监控系统在业务流程执行期间推荐干预措施，如果遵循这些干预措施，就能够预防流程出现负面结果。此类干预措施必须可靠，也就是说，它们必须保证实现期望的结果或性能；同时也必须灵活，也就是说，它们必须避免推翻正常的流程执行，或强制执行某个给定活动。然而，现有的大多数处方式流程监控解决方案虽然在推荐可靠性方面表现良好，却向用户提供必须执行的非常具体的活动（或活动序列），而没有考虑这些建议的可行性。为了解决这一问题，我们提出了一种新的面向结果的处方式流程监控系统，该系统推荐在流程执行期间必须保证的活动之间的时序关系，以实现期望的结果。这弱化了在给定时间点必须执行某项活动的强制性，从而在决定要实施哪些干预措施时为用户留下更多自由。我们的方法利用有限轨迹上的线性时序逻辑（Linear Temporal Logic over finite traces）模式来定义这些时序关系，这些模式被用作特征，用以描述由支持流程执行的信息系统记录在事件日志中的历史流程数据。这种经过编码的日志被用于训练机器学习分类器，以学习时序模式与流程执行结果之间的映射。随后在运行时查询该分类器，使其返回需要满足的最显著时序模式作为建议，从而针对输入的正在进行的流程执行，最大化某一结果的可能性。所提出的系统使用22个真实事件日志集合进行评估，这些日志已被流程挖掘社区用作基准。
英文摘要：
Prescriptive Process Monitoring systems recommend, during the execution of a business process, interventions that, if followed, prevent a negative outcome of the process. Such interventions have to be reliable, that is, they have to guarantee the achievement of the desired outcome or performance, and they have to be flexible, that is, they have to avoid overturning the normal process execution or forcing the execution of a given activity. Most of the existing Prescriptive Process Monitoring solutions, however, while performing well in terms of recommendation reliability, provide the users with very specific (sequences of) activities that have to be executed without caring about the feasibility of these recommendations. In order to face this issue, we propose a new Outcome-Oriented Prescriptive Process Monitoring system recommending temporal relations between activities that have to be guaranteed during the process execution in order to achieve a desired outcome. This softens the mandatory execution of an activity at a given point in time, thus leaving more freedom to the user in deciding the interventions to put in place. Our approach defines these temporal relations with Linear Temporal Logic over finite traces patterns that are used as features to describe the historical process data recorded in an event log by the information systems supporting the execution of the process. Such encoded log is used to train a Machine Learning classifier to learn a mapping between the temporal patterns and the outcome of a process execution. The classifier is then queried at runtime to return as recommendations the most salient temporal patterns to be satisfied to maximize the likelihood of a certain outcome for an input ongoing process execution. The proposed system is assessed using a pool of 22 real-life event logs that have already been used as a benchmark in the Process Mining community.
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