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Uncertainty arises naturally inmany application domains due to, e.g., dataentry errors and ambiguity in data cleaning. Prior work in incomplete andprobabilistic databases has investigated the semantics and efficient evaluationof ranking and top-k queries over uncertain data. However, most approaches dealwith top-k and ranking in isolation and do represent uncertain input data andquery results using separate, incompatible datamodels. We present an efficientapproach for under- and over-approximating results of ranking, top-k, andwindow queries over uncertain data. Our approach integrates well with existingtechniques for querying uncertain data, is efficient, and is to the best of ourknowledge the first to support windowed aggregation. We design algorithms forphysical operators for uncertain sorting and windowed aggregation, andimplement them in PostgreSQL.We evaluated our approach on synthetic and realworld datasets, demonstrating that it outperforms all competitors, and oftenproduces more accurate results.

Efficient Approximation of Certain and Possible Answers for Ranking and Window Queries over Uncertain Data (Extended version)