Context Tool support for business process management (BPM) is improving more and more. Often, machine learning techniques are used to recognize certain execution patterns, to optimize workflows and to observe or predict processes. Frequently, many organisations cannot meet the fundamental prerequisites of machine learning methods since less data is recorded and therefore available for analysis. Most machine learning techniques rely on big and sufficient data source sets that can be analyzed. Small Sample Learning (SSL) tackles the issue of implementing machine learning methods in environments where only quantitatively insufficient datasets are available. These methods are strongly tailored to computer vision or natural language processing problems, which is why they are still neglected in the BPM area. Objective This paper motivates the use of SSL methods in the BPM area and fosters a research stream that is concerned with the transferability to and the application of these methods in the BPM area. Method We propose a concept for leveraging SSL methods in BPM and illustrate the idea exemplarly in the field of process mining. Results Reasons for the need of SSL methods in the BPM area and a conceptual approach for transferring existing SSL methods to the BPM area. The feasibility of our apprach is shown by a brief overview of a primary study leveraging SSL methods for process prediction. Conclusions Many areas of process mining or BPM in general depend on a sufficient amount of (training) data. Often small and medium sized companies lack ”big data”, which is why advantages of machine learning and data analysis in the context of BPM cannot be applied. Existing methods that deal with insufficient data are very domain-specific and must be transferred to the process mining area respectively the BPM area.

上下文工具对业务流程管理（BPM）的支持越来越多。通常，机器学习技术用于识别某些执行模式，优化工作流程以及观察或预测过程。通常，许多组织无法满足机器学习方法的基本先决条件，因为记录的数据较少，因此可供分析。大多数机器学习技术都依赖于可以分析的足够大的数据源集。小样本学习（SSL）解决了在只有数量上不足的数据集可用的环境中实施机器学习方法的问题。这些方法非常适合计算机视觉或自然语言处理问题，因此在BPM领域仍被忽略。目的本文旨在鼓励在BPM领域使用SSL方法，并促进有关BPM领域的方法的可移植性和应用的研究。方法我们提出了一个在BPM中利用SSL方法的概念，并在过程挖掘领域中示例性地说明了这一思想。结果BPM区域需要SSL方法的原因以及将现有SSL方法转移到BPM区域的概念方法。通过使用SSL方法进行过程预测的主要研究的简要概述，表明了我们方法的可行性。结论通常，过程挖掘或BPM的许多领域都依赖于足够数量的（培训）数据。中小型公司通常缺少“大数据”，这就是为什么无法在BPM的情况下应用机器学习和数据分析的优势的原因。现有的处理数据不足的方法是特定于领域的，必须分别转移到流程挖掘区域或BPM区域。