Sciweavers

Share
SIGMOD
2008
ACM

Ranking queries on uncertain data: a probabilistic threshold approach

10 years 1 months ago
Ranking queries on uncertain data: a probabilistic threshold approach
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. Categories and Subject Descriptors H.2.4 [Systems]: Query processing General Terms Algorithm, Performance, Experimentation Keywords Uncertain Data, Probabilistic Threshold Top-k Queries, Query Processing
Ming Hua, Jian Pei, Wenjie Zhang, Xuemin Lin
Added 08 Dec 2009
Updated 08 Dec 2009
Type Conference
Year 2008
Where SIGMOD
Authors Ming Hua, Jian Pei, Wenjie Zhang, Xuemin Lin
Comments (0)
books