Probabilistic Group Nearest Neighbor Queries in Uncertain Databases

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Probabilistic Group Nearest Neighbor Queries in Uncertain Databases
The importance of query processing over uncertain data has recently arisen due to its wide usage in many real-world applications. In the context of uncertain databases, previous works have studied many query types such as nearest neighbor query, range query, top-k query, skyline query, and similarity join. In this paper, we focus on another important query, namely, probabilistic group nearest neighbor (PGNN) query, in the uncertain database, which also has many applications. Specifically, given a set, Q, of query points, a PGNN query retrieves data objects that minimize the aggregate distance (e.g., sum, min, and max) to query set Q. Due to the inherent uncertainty of data objects, previous techniques to answer group nearest neighbor (GNN) query cannot be directly applied to our PGNN problem. Motivated by this, we propose effective pruning methods, namely, spatial pruning and probabilistic pruning, to reduce the PGNN search space, which can be seamlessly integrated into our PGNN query ...
Xiang Lian, Lei Chen 0002
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where TKDE
Authors Xiang Lian, Lei Chen 0002
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