Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during the execution of a process. These event data can be used to analyze the process using process mining techniques to discover the real process, measure conformance to a given process model, or to enhance existing models with performance information. Mapping the produced events to activities of a given process model is essential for conformance checking, annotation and understanding of process mining results. In order to accomplish this mapping with low manual effort, we developed a semi-automatic approach that maps events to activities using insights from behavioral analysis and label analysis. The approach extracts Declare constraints from both the log and the model to build matching constraints to efficiently reduce the number of possible mappings. These mappings are further reduced using techniques from natural language processing, which allow for a matching based on labels and external knowledge sources. The evaluation with synthetic and real-life data demonstrates the effectiveness of the approach and its robustness toward non-conforming execution logs.

中文翻译：

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通过整合行为方面和标签分析来匹配事件和活动。

如今，业务流程越来越受到IT服务的支持，这些IT服务在流程执行期间会生成大量事件数据。这些事件数据可用于使用过程挖掘技术来分析过程，以发现实际过程，测量对给定过程模型的符合性或使用性能信息增强现有模型。将产生的事件映射到给定过程模型的活动对于一致性检查，注释和理解过程挖掘结果至关重要。为了以较少的人工完成此映射，我们开发了一种半自动方法，该方法使用行为分析和标签分析的见解将事件映射到活动。方法提取声明日志和模型中的约束来构建匹配约束，以有效减少可能的映射数量。使用自然语言处理技术可以进一步减少这些映射，该技术允许基于标签和外部知识源进行匹配。通过综合和实际数据进行的评估证明了该方法的有效性及其对不合格执行日志的鲁棒性。