This keynote speech is to all intents and purposes introducing a new process mining approach and its implemented system, which are named as eXplainable process mining (XPM) approach and system, respectively. Through the XPM approach, we can selectively amalgamate a group of process event log traces, and through the XPM system, we can ultimately discover a series of complete and safe process models from a process enactment event log dataset. The XPM approach is theoretically based on the typical process modeling methodology of structured information control nets (abbreviated as SICN) and its algorithmic functionality is substantiated by the explainable \(\rho \) -Algorithm that is extended from the original \(\rho \) -Algorithm [6]. This keynote speech is also explicating the proven process mining algorithm that is able to discover the underlying SICN-oriented process model from a dataset of process enactment event logs and histories. More specifically, the concrete goal of this keynote speech is about an explainable way of discovering the complete structural formation of SICN-oriented process model. Assume that the structural formation is made up of an arbitrary number of combinational building blocks of the primitive process patterns such as linear (sequential), disjunctive (exclusive-OR), conjunctive (parallel-AND), and repetitive (iterative-LOOP) process patterns. Conclusively, it would be emphasized that the XPM approach and system ought to be very effective and well-fitted for managing life-cycles of very large-scale process models deployed in process-aware enterprises and organizations.