Selectivity estimation in spatial databases
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
IEEE Transactions on Knowledge and Data Engineering
R-Trees: Theory and Applications (Advanced Information and Knowledge Processing)
R-Trees: Theory and Applications (Advanced Information and Knowledge Processing)
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Cost Models and Efficient Algorithms on Existentially Uncertain Spatial Data
PCI '08 Proceedings of the 2008 Panhellenic Conference on Informatics
Efficient Evaluation of Probabilistic Advanced Spatial Queries on Existentially Uncertain Data
IEEE Transactions on Knowledge and Data Engineering
On the Effect of Location Uncertainty in Spatial Querying
IEEE Transactions on Knowledge and Data Engineering
Probabilistic spatial queries on existentially uncertain data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
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A major challenge posed by real-world applications involving spatial information deals with the uncertainty inherent in the data. One type of uncertainty in spatial objects may come from their existence, which is expressed by a probability accompanying the spatial value of an object reflecting the confidence of the object's existence. A challenging query type over existentially uncertain data is the search of the Nearest Neighbour (NN), as the likelihood of an object to be the NN of the query object does not only depend on its distances from other objects, but also from their existence. In this paper, we present exact and approximate statistical methodologies for supporting cost models for Probabilistic Thresholding NN (PTNN) queries that deal with arbitrarily distributed data points and existential uncertainty, with the aid of appropriate novel histograms, sampling and statistical approximations. Our cost model can be also modified in order to support Probabilistic Ranking NN (PRNN) queries with the aid of sampling. The accuracy of our approaches is exhibited through extensive experimentation on synthetic and real datasets.