Shooting top- k stars in uncertain databases

Shooting top- k stars in uncertain databases Query processing in the uncertain database has played an important role in many real-world applications due to the wide existence of uncertain data. Although many previous techniques can correctly handle precise data, they are not directly applicable to the uncertain scenario. In this article, we investigate and propose a novel query, namely probabilistic top-k star (PT k S) query, which aims to retrieve k objects in an uncertain database that are “closest” to a static/dynamic query point, considering both distance and probability aspects. In order to efficiently answer PT k S queries with a static/moving query point, we propose effective pruning methods to reduce the PT k S search space, which can be seamlessly integrated into an efficient query procedure. Finally, extensive experiments have demonstrated the efficiency and effectiveness of our proposed PT k S approaches on both real and synthetic data sets, under various parameter settings. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Shooting top- k stars in uncertain databases

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Publisher
Springer-Verlag
Copyright
Copyright © 2011 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-011-0225-y
Publisher site
See Article on Publisher Site

Abstract

Query processing in the uncertain database has played an important role in many real-world applications due to the wide existence of uncertain data. Although many previous techniques can correctly handle precise data, they are not directly applicable to the uncertain scenario. In this article, we investigate and propose a novel query, namely probabilistic top-k star (PT k S) query, which aims to retrieve k objects in an uncertain database that are “closest” to a static/dynamic query point, considering both distance and probability aspects. In order to efficiently answer PT k S queries with a static/moving query point, we propose effective pruning methods to reduce the PT k S search space, which can be seamlessly integrated into an efficient query procedure. Finally, extensive experiments have demonstrated the efficiency and effectiveness of our proposed PT k S approaches on both real and synthetic data sets, under various parameter settings.

Journal

The VLDB JournalSpringer Journals

Published: Dec 1, 2011

References

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