AI 中文总结
本文针对跨组织事件日志获取困难的问题，提出将消息日志转换为保留消息事件因果顺序的事件日志，以合成工业网的跨组织过程挖掘方法。
AI 中文摘要
企业网（Enterprise nets）和工业网（Industry nets）是特定的Petri网模型，共同构成了用于建模协作业务流程及指定流程间交互的框架。在此架构中，单个业务流程被建模为企业网，而工业网则由通过单向通道交换消息进行交互的企业网组成。在之前的研究成果（见文献[21,22]）中，已明确如何利用从事件日志（由执行动作的有限序列构成）中发现单个流程（建模为企业网）的算法，来合成由异步通信流程（建模为工业网）组成的系统。然而，通常情况下，处理来自不同组织的事件日志或获取其访问权限可能存在困难，而记录流程间的消息交换可能更为可行。本文聚焦于不同流程间的通信，研究消息日志——即两个流程间以消息交换（输出和输入事件）形式呈现的通信记录集合。特别地，本文探讨如何将消息日志转换为事件日志形式的通信行为描述，以保留消息事件间的因果顺序。遵循文献[21,22]的方法，该事件日志可用于合成工业网。
英文摘要
Enterprise nets and Industry nets are specific Petri net models that together form a framework for the modelling of collaborating business processes and the specification of interactions between them. In this set-up, individual business process are modelled as Enterprise nets, while Industry nets consist of Enterprise nets that interact by exchanging messages through unidirectional channels. In previous contributions (see [21,22]), it was established how to leverage algorithms for the discovery of single processes (modelled as Enterprise nets) from event logs (consisting of finite sequences of executed actions), to synthesise systems consisting of asynchronously communicating processes (modelled as Industry nets). However, in general, it may be difficult to work with event logs from different organisations or even get access to them. Recordings of message exchanges between processes may be easier available.Therefore, we focus in this paper on the communications between different processes and turn to the investigation of message logs, \ie collections of recordings of communications in the form of message exchanges (output and input events) between two processes. In particular, the question is addressed of how a message log can be transformed into a description of the communication behaviour in the form of an event log such that the causal order on message events is preserved. Following the approach from [21,22], this event log can then be used to synthesis an Industry net.
CommentsThis paper is the full version of an extended abstract with the same title, presented at the third International workshop on Collaboration Mining for Distributed Systems (COMINDS 2024) held in conjunction with ICPM 2024 in Copenhagen
