Probabilistic spatial queries on existentially uncertain data
Xiangyuan Dai,Man Lung Yiu,Nikos Mamoulis,Yufei Tao,Michail Vaitis +4 more
- Vol. 3633, pp 400-417
TLDR
This work proposes adaptations of spatial access methods and search algorithms for probabilistic versions of range queries and nearest neighbors and conducts an extensive experimental study, which evaluates the effectiveness of proposed solutions.Abstract:
We study the problem of answering spatial queries in databases where objects exist with some uncertainty and they are associated with an existential probability. The goal of a thresholding probabilistic spatial query is to retrieve the objects that qualify the spatial predicates with probability that exceeds a threshold. Accordingly, a ranking probabilistic spatial query selects the objects with the highest probabilities to qualify the spatial predicates. We propose adaptations of spatial access methods and search algorithms for probabilistic versions of range queries and nearest neighbors and conduct an extensive experimental study, which evaluates the effectiveness of proposed solutions.read more
Citations
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The new Casper: query processing for location services without compromising privacy
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A tree-based approach for frequent pattern mining from uncertain data
TL;DR: A tree-based mining algorithm is proposed to efficiently find frequent patterns from uncertain data, where each item in the transactions is associated with an existential probability.
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Location-dependent query processing: Where we are and where we are heading
TL;DR: The technological context (mobile computing) and support middleware (such as moving object databases and data stream technology) are described, location-based services and location-dependent queries are defined and classified, and different query processing approaches are reviewed and compared.
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