Process mining is an emerging discipline that aims to analyze business processes using event data logged by IT systems. In process mining, the focus is on how to effectively and efficiently predict the next process/trace to be activated among all the possible processes/traces that are available in the process schema (usually modeled as a graph). Most of the existing process mining techniques assume that there is a one-to-one mapping between process model activities and the events that are recorded during process execution. However, event logs and process model activities are at different level of granularity. In this paper, we present a machine-learning-based approach to map low-level event logs to high-level activities. With this work, we can bridge the abstraction levels when the high-level labels of the low-level events are not available. The proposed approach consists of two main phases: automatic labeling and machine-learning-based classification. In automatic labeling, a modified k-prototypes clustering approach has been used in order to obtain the labeled examples. Then, in the second phase, we trained different ML classifiers using the obtained labeled examples. Since, in real-life applications and systems, business processes are expressed according to the Business Process Model and Notation (BPMN) format, we improve our proposed framework by means of an innovative, flexible BPMN model translation methodology that acts at the first phase. We demonstrate the applicability of our proposed framework using two case studies with real-world event logs, and provide its experimental assessment and analysis.

流程挖掘是一门新兴学科，旨在使用IT系统记录的事件数据来分析业务流程。在流程挖掘中，重点在于如何有效地，有效地预测流程模式中可用的所有可能的流程/痕迹（通常建模为图形）中要激活的下一个流程/痕迹。大多数现有的流程挖掘技术都假设流程模型活动与流程执行期间记录的事件之间存在一对一的映射。但是，事件日志和流程模型活动的粒度不同。在本文中，我们提出了一种基于机器学习的方法，可将低级事件日志映射到高级活动。通过这项工作，当低级事件的高级标签不可用时，我们可以桥接抽象级别。所提出的方法包括两个主要阶段：自动标记和基于机器学习的分类。在自动贴标中，为了获得标记的示例，已使用k-原型聚类方法。然后，在第二阶段，我们使用获得的标记示例训练了不同的ML分类器。由于在现实生活中的应用程序和系统中，业务流程是根据业务流程模型和表示法（BPMN）格式表示的，因此我们通过在第一阶段起作用的创新，灵活的BPMN模型转换方法来改进我们提出的框架。我们使用两个带有真实事件日志的案例研究证明了我们提出的框架的适用性，并提供了实验评估和分析。