ExAnte is a simple yet effective approach for preprocessing input data for mining frequent patterns. The approach questions established research in that it requires no trade-off between antimonotonicity and monotonicity. Indeed, ExAnte relies on a strong synergy between these two opposite components and exploits it to dramatically reduce the data being analyzed to that containing interesting patterns. This data reduction, in turn, induces a strong reduction of the candidate patterns' search space. The result is significant performance improvements in subsequent mining. It can also make feasible some otherwise intractable mining tasks. The authors describe their technology and experiments that proved its effectiveness using different constraints on various data sets.