PAIS Lab seminar: Causal nets – a modeling language designed for process discovery // N. Gundobin

HSE building at Kirpichnaya street, 33 - aud. 902

Nikita Gundobin (PAIS Lab) presented the talk on subject "Causal nets – a modeling language designed for process discovery".


A b s t r a c t

Causal net (C-net) is a relatively new process modeling language that provides a better representational bias for process discovery comparing with traditional models, such as Petri nets, BPMN, YAWL. Due to declarative semantics of Causal nets, the model allows to avoid synchronization conflicts (deadlocks and livelocks). Therefore, C-nets tend to be suitable for process discovery and they are used in several process discovery techniques: heuristic mining, fuzzy mining and genetic mining. Despite the great suitability for process discovery, causal nets have a complex semantics that leads to unobvious representation for control flow analysis.

This talk will cover the detailed description of C-nets’ semantics and features that makes them suitable for process discovery. Moreover, the problems of converting causal nets to Workflow nets and BPMN-models will be mentioned.