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Most of today's structured data is stored in relational databases. Such a database consists of multiple relations which are linked together conceptually via entity-relationship links in the design of relational database schemas. Multirelational classification can be widely used in many disciplines, such as financial decision-making, medical research, and geographical applications. However, most classification approaches only work on single "flat" data relations. It is usually difficult to convert multiple relations into a single flat relation without either introducing huge, undesirable "universal relation" or losing essential information. Previous works using inductive logic programming approaches (recently also known as relational mining) have proven effective with high accuracy in multi-relational classification. Unfortunately, they suffer from poor scalability w.r.t. the number of relations and the number of attributes in databases. We propose CrossMine, an efficient and scalable approach for multirelational classification. Several novel methods are developed in CrossMine, including (1) tuple ID propagation, which performs semantics-preserving virtual join to achieve high efficiency on databases with complex schemas, and (2) a selective sampling method, which makes it highly scalable w.r.t. the number of tuples in the databases. Both theoretical backgrounds and implementation techniques of CrossMine are introduced. Our comprehensive experiments on both real and synthetic databases demonstrate the high scalability and accuracy of CrossMine.