Ranking Queries on Uncertain Data: A Probabilistic Threshold Approach

Ranking Queries on Uncertain Data: A Probabilistic Threshold Approach (2008)

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by Ming Hua , Jian Pei , Wenjie Zhang , Xuemin Lin

Venue:	Computer Science Department, Florida State University
Citations:	47 - 10 self

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@TECHREPORT{Hua08rankingqueries,
    author = {Ming Hua and Jian Pei and Wenjie Zhang and Xuemin Lin},
    title = {Ranking Queries on Uncertain Data: A Probabilistic Threshold Approach},
    institution = {Computer Science Department, Florida State University},
    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.

Citations

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