在提供保证和过滤低频行为模式的同时发现具有长期依赖的过程模型
Discovering Process Models With Long-Term Dependencies While Providing Guarantees and Filtering Infrequent Behavior Patterns
AI总结：
本文改进eST-Miner算法，通过选择子集Places保证Petri网的最小适应度并保持高精度，同时引入改进的Place适应度度量以避免低频活动标签的阻塞。
摘要：
在过程发现中，目标是为给定事件日志找到描述底层过程的模型。尽管过程模型可以以多种方式表示，但Petri网作为一种理论上经过深入研究的描述语言，因此常被使用。在本文中，我们扩展了eST-Miner过程发现算法。eST-Miner计算一组Petri网的places，这些places被认为与给定事件日志所描述的行为的一定比例相吻合，这由给定的噪声阈值指示。它通过基于令牌的回放评估所有可能的候选places。每个place的可回放轨迹集是独立确定的，即这些集不需要一致。这允许算法抽象出只在某些轨迹中出现的低频行为模式。然而，当通过连接到相应唯一标记的转换将places组合成Petri网时，所得到的网可以回放恰好由所有插入places的组合允许的事件日志中的轨迹。因此，逐个插入places而不考虑其综合效果可能导致死锁和低适应度的Petri网。在本文中，我们探讨了eST-Miner的改进方法，旨在选择一组places，使得所得到的Petri网保证具有可定义的最小适应度，同时保持对输入事件日志的高精度。此外，当前places评估技术倾向于阻塞低频活动标签的执行。因此，引入了改进的places适应度度量并进行了彻底研究。在我们的实验中，我们使用真实和人工事件日志来评估和比较各种places选择策略和places适应度评估度量对返回的Petri网的影响。
英文摘要：
In process discovery, the goal is to find, for a given event log, the model describing the underlying process. While process models can be represented in a variety of ways, Petri nets form a theoretically well-explored description language and are therefore often used. In this paper, we extend the eST-Miner process discovery algorithm. The eST-Miner computes a set of Petri net places which are considered to be fitting with respect to a certain fraction of the behavior described by the given event log as indicated by a given noise threshold. It evaluates all possible candidate places using token-based replay. The set of replayable traces is determined for each place in isolation, i.e., these sets do not need to be consistent. This allows the algorithm to abstract from infrequent behavioral patterns occurring only in some traces. However, when combining places into a Petri net by connecting them to the corresponding uniquely labeled transitions, the resulting net can replay exactly those traces from the event log that are allowed by the combination of all inserted places. Thus, inserting places one-by-one without considering their combined effect may result in deadlocks and low fitness of the Petri net. In this paper, we explore adaptions of the eST-Miner, that aim to select a subset of places such that the resulting Petri net guarantees a definable minimal fitness while maintaining high precision with respect to the input event log. Furthermore, current place evaluation techniques tend to block the execution of infrequent activity labels. Thus, a refined place fitness metric is introduced and thoroughly investigated. In our experiments we use real and artificial event logs to evaluate and compare the impact of the various place selection strategies and place fitness evaluation metrics on the returned Petri net.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
