NDLI: Evaluating probability threshold k-nearest-neighbor queries over uncertain data

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Evaluating probability threshold k-nearest-neighbor queries over uncertain data

Content Provider	ACM Digital Library
Author	Chen, Lei Xie, Xike Cheng, Reynold Chen, Jinchuan
Abstract	In emerging applications such as location-based services, sensor monitoring and biological management systems, the values of the database items are naturally imprecise. For these uncertain databases, an important query is the Probabilistic k-Nearest-Neighbor Query (k-PNN), which computes the probabilities of sets of k objects for being the closest to a given query point. The evaluation of this query can be both computationally- and I/O-expensive, since there is an exponentially large number of k object-sets, and numerical integration is required. Often a 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 Probabilistic Threshold k-Nearest-Neighbor Query (T-k-PNN), which returns sets of k objects that satisfy the query with probabilities higher than some threshold T. Three steps are proposed to handle this query efficiently. In the first stage, objects that cannot constitute an answer are filtered with the aid of a spatial index. The second step, called probabilistic candidate selection, significantly prunes a number of candidate sets to be examined. The remaining sets are sent for verification, which derives the lower and upper bounds of answer probabilities, so that a candidate set can be quickly decided on whether it should be included in the answer. We also examine spatially-efficient data structures that support these methods. Our solution can be applied to uncertain data with arbitrary probability density functions. We have also performed extensive experiments to examine the effectiveness of our methods.
Starting Page	672
Ending Page	683
Page Count	12
File Format	PDF
ISBN	9781605584225
DOI	10.1145/1516360.1516438
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-03-24
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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