Discovering Structural Formations of Multi-Level and Compound Control-Flow Gateways From Process Logs

In the field of process mining, the discovery of process patterns from event logs remains a challenging topic and has always interested many researchers. Exploiting the process model remains a major challenge and is highly dependent on event log characteristics, such as dataset size, the completeness of the event trace, and especially the complexity of the process model structure. The
$\rho $
(rho)-algorithm is a powerful process mining algorithm that can mine all the structured information control net (SICN), such as sequential, selective, parallel, and iterative. The
$\rho $
-algorithm also faces challenges when mining complex process patterns, consisting of many SICN primitive patterns combined at multiple levels that satisfy properties matched pairing and the proper nesting. This paper presents a new approach, defines the formalization of the multi-level and compound control-flow gateways (ML-CCFG) and a series of rules for decision-making these gateways, and proposes an algorithm extending from
$\rho $
-algorithm (we named it
$\rho $
MC-algorithm), that can efficiently discover the SICN-oriented process model containing ML-CCFG from process instances event log. We developed an implementation system
$\rho $
MC-algorithm to discover and visualize in a graphical form SICN-oriented process models from the datasets of the IEEE XES-formatted process enactment event logs. We also perform a series of experimental analyses using the implemented system on the process enactment event log datasets to verify the proposed algorithm.