Visualization and Intelligent Systems Laboratory
VISLab

 

 

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VISLab
Winston Chung Hall Room 216
University of California, Riverside
900 University Avenue
Riverside, CA 92521-0425


Tel: (951)-827-3954

CRIS
Bourns College of Engineering
UCR
NSF IGERT on Video Bioinformatics

UCR Collaborators:
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ME
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ENTM
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IEEE Biometrics Workshop 2014
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Alex Shin
wshin@ece.ucr.edu

Last updated: July 1, 2017

 

 

Managing and Manipulating Uncertainty in Spatial Databases

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.