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Presented by: Rui Li
Abstract:
Managing and manipulating uncertainty in spatial databases
are important problems for various practical applications.
Unlike the traditional fuzzy approaches in relational databases,
in this paper we use a probability-based method to model and
index uncertain spatial data where every object is represented
by a probability density function(PDF). To index PDFs, we
construct an optimized Gaussian mixture hierarchy (OGMH) and
two variants of uncertain R-tree. A comprehensive comparison
among these three indices and plain R-tree is done on TIGER/Line
Southern California landmark point dataset. We find that uncertain
R-tree is the best for fixed query and OGMH is suitable for
both certain and uncertain queries. Moreover, OGMH is suitable
not only for spatial databases, but also for multi-dimensional
indexing applications like content based image retrieval,
where R-tree is inefficient in high dimensions.
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