Process events are recorded by multiple information systems at different granularity levels. Based on the resulting event logs, process models are discovered at different granularity levels, as well. Events stored at a fine-grained granularity level, for example, may hinder the discovered process model to be displayed due the high number of resulting model elements. The discovered process model of a real-world manufacturing process, for example, consists of 1,489 model elements and over 2,000 arcs. Existing process model abstraction techniques could help reducing the size of the model, but would disconnect it from the underlying event log. Existing event abstraction techniques do neither support the analysis of mixed granularity levels, nor interactive exploration of a suitable granularity level. To enable the exploration of discovered process models at different granularity levels, we propose INEXA, an interactive, explainable process model abstraction method that keeps the link to the event log. As a starting point, INEXA aggregates large process models to a "displayable" size, e.g., for the manufacturing use case to a process model with 58 model elements. Then, the process analyst can explore granularity levels interactively, while applied abstractions are automatically traced in the event log for explainability.

翻译：过程事件由多个信息系统以不同粒度级别记录。基于由此产生的事件日志，过程模型也在不同粒度级别上被发现。例如，以细粒度级别存储的事件可能会因生成的大量模型元素而阻碍所发现过程模型的展示。以实际制造过程为例，其发现的过程模型包含1,489个模型元素和超过2,000条弧线。现有的过程模型抽象技术虽有助于缩小模型规模，但会导致模型与底层事件日志脱节。现有的事件抽象技术既不支持混合粒度级别的分析，也不支持对合适粒度级别的交互式探索。为实现对不同粒度级别下所发现过程模型的探索，我们提出了INEXA——一种交互式、可解释的过程模型抽象方法，该方法保持与事件日志的关联。作为起点，INEXA将大型过程模型聚合至“可展示”规模，例如针对制造用例，可将其简化为包含58个模型元素的过程模型。随后，过程分析师可交互式探索粒度级别，同时所应用的抽象操作会被自动记录到事件日志中，以实现可解释性。