Probabilistic range query is an important type of query in the area of uncertain data management. A probabilistic range query returns all the objects within a specific range from the query object with a probability no less than a given threshold. In this paper we assume that each uncertain object stored in the databases is associated with a multi-dimensional Gaussian distribution, which describes the probability distribution that the object appears in the multi-dimensional space. A query object is either a certain object or an uncertain object modeled by a Gaussian distribution. We propose several filtering techniques and an R-tree-based index to efficiently support probabilistic range queries over Gaussian objects. Extensive experiments on real data demonstrate the efficiency of our proposed approach. © 2013 Springer-Verlag.
CITATION STYLE
Dong, T., Xiao, C., Guo, X., & Ishikawa, Y. (2013). Processing probabilistic range queries over Gaussian-based uncertain data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8098 LNCS, pp. 410–428). https://doi.org/10.1007/978-3-642-40235-7_24
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