Top-k representative skyline queries are important for multi-criteria decision making applications since they provide an intuitive way to identify the k most significant objects for data analysts. Despite their importance, top-/: representative skyline queries have not received adequate attention from the research community. Existing work addressing the problem focuses only on certain data models. For this reason, in this paper, we present the first study on processing top-k representative skyline queries in uncertain databases, based on user-defined references, regarding the priority of individual dimensions. We also apply the odds ratio to restrict the cardinality of the result set, instead of using a threshold which might be difficult for an end-user to define. We then develop two novel algorithms for answering top-k representative skyline queries on uncertain data. In addition, several pruning conditions are proposed to enhance the efficiency of our proposed algorithms. Performance evaluations are conducted on both real-life and synthetic datasets to demonstrate the efficiency, effectiveness and scalability of our proposed approaches.
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