2013 | OriginalPaper | Buchkapitel
Efficient Probabilistic Reverse k-Nearest Neighbors Query Processing on Uncertain Data
verfasst von : Jiajia Li, Botao Wang, Guoren Wang
Erschienen in: Database Systems for Advanced Applications
Verlag: Springer Berlin Heidelberg
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A reverse
k
-nearest neighbors (
RkNN
) query returns all the objects that take the query object
q
as their
k
nearest neighbors. However, data are often uncertain in numerous applications. In this paper, we focus on the problem of processing
RkNN
on uncertain data. A probabilistic
RkNN
(
PRkNN
) query retrieves all the objects that have higher probabilities than a user-specified threshold to be the
RkNN
of
q
. The previous work for answering
PRNN
query are mainly based on the distance relationship between uncertain objects, and are inapplicable for
PRkNN
when
k
> 1. In this paper, we design a novel algorithm for
PRkNN
query to support arbitrary values of
k
on the basis of two pruning strategies, namely spatial pruning and probabilistic pruning. The spatial pruning rule is defined on both the distances and the angle ranges between uncertain objects. And an efficient upper bound of probability is estimated by the probabilistic pruning algorithm. Extensive experiments are conducted to study the performance of the proposed approach. The results show that our proposed algorithm has a better performance and scalability than the existing solution regarding the growth of
k
.