Sequential pattern mining (SPM) is a well‐studied theme in data mining, in which one aims to discover common sequences of item sets in a large corpus of temporal itemset data. Due to the sequential nature of data streams, supporting SPM in streaming environments is commonly studied in the area of data stream mining as well. On the other hand, stream‐based process discovery (PD), originating from the field of process mining, focusses on learning process models on the basis of online event data. In particular, the main goal of the models discovered is to describe the underlying generating process in an end‐to‐end fashion. As both SPM and PD use data that are comparable in nature, that is, both involve time‐stamped instances, one expects that techniques from the SPM domain are (partly) transferable to the PD domain. However, thus far, little work has been done in the intersection of the two fields. In this focus article, we therefore study the possible application of SPM techniques in the context of PD. We provide an overview of the two fields, covering their commonalities and differences, highlight the challenges of applying them, and, present an outlook and several avenues for future work.

中文翻译：

---

关于顺序模式挖掘原语在过程发现中的应用：概述，前景和机会识别

顺序模式挖掘（SPM）是数据挖掘中经过充分研究的主题，其目的是发现大型时态项目集数据中项目集的常见序列。由于数据流的顺序性质，在数据流挖掘领域也普遍研究了在流环境中支持SPM。另一方面，源于过程挖掘领域的基于流的过程发现（PD）专注于基于在线事件数据的学习过程模型。特别是，发现的模型的主要目标是以端到端的方式描述潜在的生成过程。由于SPM和PD都使用本质上可比较的数据，也就是说，两者都涉及带时间戳的实例，因此人们希望SPM域中的技术可以（部分）转移到PD域。但是到目前为止 在这两个领域的交集中，几乎没有做任何工作。因此，在本重点文章中，我们将研究SPM技术在PD环境中的可能应用。我们对这两个领域进行了概述，涵盖了它们的共性和差异，突出了应用它们的挑战，并提出了展望和未来工作的几种途径。