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Top-k processing in uncertain databases is semantically and computationally different from traditional top-k processing. The interplay between score and uncertainty information makes traditional top-k processing techniques inapplicable to uncertain databases. In this paper the authors introduce new probabilistic formulations for top-k queries. The formulations are based on marriage of traditional top-k semantics with possible worlds semantics. In the light of these formulations, they construct a framework that encapsulates a novel probabilistic model and efficient query processing techniques to tackle the challenges raised by uncertain data settings. They prove that the techniques minimize the number of accessed tuples, and the number of materialized possible query answers.
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