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Probabilistic Verifiers: Evaluating Constrained Nearest-Neighbor Queries over Uncertain Data

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Probabilistic Verifiers: Evaluating Constrained Nearest-Neighbor Queries over Uncertain Data
In applications like location-based services, sensor monitoring and biological databases, the values of the database items are inherently uncertain in nature. An important query for uncertain objects is the Probabilistic Nearest-Neighbor Query (PNN), which computes the probability of each object for being the nearest neighbor of a query point. Evaluating this query is computationally expensive, since it needs to consider the relationship among uncertain objects, and requires the use of numerical integration or Monte-Carlo methods. Sometimes, a query user may not be concerned about the exact probability values. For example, he may only need answers that have sufficiently high confidence. We thus propose the Constrained Nearest-Neighbor Query (C-PNN), which returns the IDs of objects whose probabilities are higher than some threshold, with a given error bound in the answers. The C-PNN can be answered efficiently with probabilistic verifiers. These are methods that derive the lower and up...
Reynold Cheng, Jinchuan Chen, Mohamed F. Mokbel, C
Added 20 Dec 2008
Updated 20 Dec 2008
Type Conference
Year 2008
Where ICDE
Authors Reynold Cheng, Jinchuan Chen, Mohamed F. Mokbel, Chi-Yin Chow.
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