Process mining traditionally relies on input consisting of low-level events that capture individual activities, such as filling out a form or processing a product. However, many of the complex problems inherent in processes, such as bottlenecks and compliance issues, extend beyond the scope of individual events and process instances. Consider congestion, for instance, it can involve and impact numerous cases, much like how a traffic jam affects many cars simultaneously. High-level event mining seeks to address such phenomena using the regular event data available. This report offers an extensive and comprehensive overview at existing work and challenges encountered when lifting the perspective from individual events and cases to system-level events.

翻译：传统过程挖掘通常依赖于捕获个体活动（如填写表格或处理产品）的低层次事件作为输入。然而，流程中固有的许多复杂问题（如瓶颈与合规性问题）往往超出了单个事件和流程实例的范畴。以拥堵为例，它可能涉及并影响大量案例，正如交通堵塞会同时影响多辆汽车。高层次事件挖掘旨在利用现有常规事件数据来解决此类现象。本报告对现有研究进行了广泛而全面的综述，并探讨了将视角从个体事件和案例提升至系统层次事件时所面临的挑战。