Advanced Data Mining applications require more and more support from relational database engines. Especially clustering applications in high dimensional features space demand a proper support of multiple Top-k queries in order to perform projected clustering. Although some research tackles to problem of optimizing restricted ranking (top-k) queries, there is no solution considering more than one single ranking criterion. This deficit - optimizing multiple Topk queries over joins - is targeted by this paper from two perspectives. On the one hand, we propose a minimal but quite handy extension of SQL to express multiple top-k queries. On the other hand, we propose an optimized hash join strategy to efficiently execute this type of queries. Extensive experiments conducted in this context show the feasibility of our proposal.