This paper addresses a fundamental and challenging problem with broad applications: efficient processing of region-based promotion queries, i.e., to discover the top-k most interesting regions for effective promotion of an object (e.g., a product or a person) given by user, where a region is defined over continuous ranged dimensions. In our problem context, the object can be promoted in a region when it is top-ranked in it. Such type of promotion queries involve an exponentially large search space and expensive aggregation operations. For efficient query processing, we study a fresh, principled framework called region-based promotion cube (RepCube). Grounded on a solid cost analysis, we first develop a partial materialization strategy to yield the provably maximum online pruning power given a storage budget. Then, cell relaxation is performed to further reduce the storage space while ensuring the effectiveness of pruning using a given bound. Extensive experiments conducted on larg...