Probabilistic Group Nearest Neighbor Queries in Uncertain Databases

ABSTRACT:

The importance of query processing over uncertain information has recently arisen thanks to its wide usage in many real-world applications. Within the context of uncertain databases, previous work have studied several query types like nearest neighbor query, vary question, top-$k$ question, skyline query, and similarity be part of. In this paper, we have a tendency to specialize in another necessary query, particularly probabilistic group nearest neighbor query (PGNN), within the uncertain database, which conjointly has several applications. Specifically, given a set, Q, of question points, a PGNN question retrieves data objects that minimize the aggregate distance (e.g. total, min, and max) to question set Q. Thanks to the inherent uncertainty of data objects, previous techniques to answer group nearest neighbor question (GNN) can't be directly applied to our PGNN problem. Motivated by this, we tend to propose effective pruning strategies, specifically spatial pruning and probabilistic pruning, to scale back the PGNN search space, which will be seamlessly integrated into our PGNN question procedure. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approach, in terms of the wall clock time and the speed-up ratio against linear scan.

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