Ranking queries on uncertain data: a probabilistic threshold approach

Authors:
Ming Hua;Jian Pei;Wenjie Zhang;Xuemin Lin
Affiliations:
Simon Fraser University, Burnaby, BC, Canada;Simon Fraser University, Burnaby, BC, Canada;The University of New South Wales & NICTA, Sydney, Australia;The University of New South Wales & NICTA, Sydney, Australia
Venue:
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Year:
2008

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Abstract

Uncertain data is inherent in a few important applications such as environmental surveillance and mobile object tracking. Top-k queries (also known as ranking queries) are often natural and useful in analyzing uncertain data in those applications. In this paper, we study the problem of answering probabilistic threshold top-k queries on uncertain data, which computes uncertain records taking a probability of at least p to be in the top-k list where p is a user specified probability threshold. We present an efficient exact algorithm, a fast sampling algorithm, and a Poisson approximation based algorithm. An empirical study using real and synthetic data sets verifies the effectiveness of probabilistic threshold top-k queries and the efficiency of our methods.