INEXA: Interactive and Explainable Process Model Abstraction Through Object-Centric Process Mining
This addresses the challenge for process analysts in handling complex event data by enabling interactive exploration of granularity levels, though it is incremental over existing abstraction techniques.
The paper tackles the problem of large, unreadable process models from event logs by proposing INEXA, an interactive and explainable abstraction method that reduces model size while maintaining links to the event log, demonstrated by reducing a manufacturing process model from 1,489 elements to 58.
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.