Efficient pruning algorithm for top-K ranking on dataset with value uncertainty

Abstract

Top-K ranking query in uncertain databases aims to find the top-K tuples according to a ranking function. The interplay between score and uncertainty makes top-K ranking in uncertain databases an intriguing issue, leading to rich query semantics. Recently, a unified ranking framework based on parameterized ranking functions (PRFs) is formulated, which generalizes many previously proposed ranking semantics. Under the PRFs based ranking framework, efficient pruning approach for Top-K ranking on dataset with tuple uncertainty has been well studied in the literature. However, this cannot be applied to top-K ranking on dataset with value uncertainty (described through attribute-level uncertain data model), which are often natural and useful in analyzing uncertain data in many applications. This paper aims to develop efficient pruning techniques for top-K ranking on dataset with value uncertainty under the PRFs based ranking framework, which has not been well studied in the literature. We present the mathematics of deriving the pruning techniques and the corresponding algorithms. The experimental results on both real and synthetic data demonstrate the effectiveness and efficiency of the proposed pruning techniques.