Process mining aims at obtaining information about processes by analysing their past executions in event logs, event streams, or databases. Discovering a process model from a finite amount of event data thereby has to correctly infer infinitely many unseen behaviours. Thereby, many process discovery techniques leverage abstractions on the finite event data to infer and preserve behavioural information of the underlying process. However, the fundamental information-preserving properties of these abstractions are not well understood yet. In this paper, we study the information-preserving properties of the “directly follows” abstraction and its limitations. We overcome these by proposing and studying two new abstractions which preserve even more information in the form of finite graphs. We then show how and characterize when process behaviour can be unambiguously recovered through characteristic footprints in these abstractions. Our characterization defines large classes of practically relevant processes covering various complex process patterns. We prove that the information and the footprints preserved in the abstractions suffice to unambiguously rediscover the exact process model from a finite event log. Furthermore, we show that all three abstractions are relevant in practice to infer process models from event logs and outline the implications on process mining techniques.

中文翻译：

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流程挖掘中事件数据的信息保留抽象

流程挖掘旨在通过分析事件日志，事件流或数据库中的过去执行情况来获取有关过程的信息。因此，从有限数量的事件数据中发现过程模型必须正确地推断出许多看不见的行为。因此，许多过程发现技术都利用了抽象在有限事件数据上推断和保留基础流程的行为信息。但是，这些抽象的基本信息保存属性尚未得到很好的理解。在本文中，我们研究了“直接遵循”抽象的信息保留性质及其局限性。我们通过提出和研究两个新的抽象来克服这些问题，它们以有限图的形式保留了更多的信息。然后，我们展示如何通过这些抽象中的特征足迹明确地恢复过程行为，并进行表征。我们的特性定义了涵盖各种复杂过程模式的大量实用的相关过程。我们证明抽象中保留的信息和足迹足以从有限事件日志中明确地重新发现确切的过程模型。此外，我们展示了在实践中从事件日志中推断过程模型并概述对过程挖掘技术的影响的所有三个抽象都是相关的。