Top-k Query Evaluation with Probabilistic Guarantees Martin Theobald, Gerhard Weikum, and Ralf Schenkel   View Paper (PDF)   Return to RESEARCH SESSION 17:TOP-K RANKING Abstract Top-k queries based on ranking elements of multidimensional datasets are a fundamental building block for many kinds of information discovery. The best known general-purpose algorithm for evaluating top-k queries is Fagin's threshold algorithm (TA). Since the user's goal behind top-k queries is to identify one or a few relevant and novel data items, it is intriguing to use approximative variants of TA to reduce run-time costs. This paper introduces a family of approximative top-k algorithms based on probabilistic arguments. When scanning index lists of the underlying multidimensional data space in descending order of local scores, various forms of convolution and derived bounds are employed to predict when it is safe, with high probability, to drop candidate items and to prune the index scans. The precision and the efficiency of the developed methods are experimentally evaluated based on a large Web corpus and a structured data collection. ©2005 Association for Computing Machinery