Most of the existing work on skyline query has been extensively used in decision support, recommending systems etc, and mainly focuses on the efficiency issue for a single table. However the data retrieved by users for the targeting skylines may often be stored in multiple tables, thus require to perform join operations among tables. As a result, the cost on computing skylines on the joined table will be increased dramatically due to its potentially increasing cardinality and dimensionality. In this paper, we systematically study the skyline operator on multi-relational databases, and propose solutions aiming to seamlessly integrating state-of-the-art join methods into skyline computation. Our experiments not only demonstrate that the proposed methods are efficient, but also show the promising applicability of extending skyline operator to other typical database operators such as join and aggregates.