The document discusses region-based process discovery techniques. It aims to obtain a formal model from a set of system executions to represent the processes conducted in a system. The theory of regions is useful for process discovery by learning a formal model (Petri nets) from a set of event traces. Revealing underlying cycles and folding an initial automaton can significantly reduce the complexity of region-based techniques. The paper incorporates region information into a cycle detection algorithm to efficiently identify complex cycles not obtainable with current techniques. Experimental results suggest the presented techniques widen application of region theory for industrial process mining scenarios.