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Growing interests in multi-criteria decision support applications have resulted in a flurry of efficient skyline algorithms. In practice, real-world decision support applications require to access data from disparate sources. Existing techniques define the skyline operation to work on a single set, and therefore, treat skylines as an “add-on" on top of a traditional Select-Project-Join query plan. In many real world applications, the skyline dimensions can be anticorrelated (e.g., attribute pairs {price, mileage} for cars or {price, distance} for hotels). Anti-correlated data are skyline-unfriendly and thus ignored by existing techniques. In this work, we propose a robust execution framework called SKIN to evaluate skyline over joins. The salient features of SKIN are: (a) effective in reducing the two primary costs, namely the cost of generating the join results and the cost of dominance comparisons to compute the final skyline of join results, (b) shown to be robust for both skyline-friendly (independent and correlated) as well as skyline-unfriendly (anticorrelated) data distributions. SKIN is effective in exploiting the skyline knowledge in both local (individual data source), as well as across disparate sources – to significantly reduce the above mentioned two primary costs incurred during the evaluation of skyline over join. Our experimental study demonstrates the superiority of our proposed approach over state-of-the-art techniques to handle a wide variety of data distributions.