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 the theory of regions for industrial Process Mining scenarios.