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.
The VLDB Journal – Springer Journals
Published: Dec 1, 2011
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