Visualization and Intelligent Systems Laboratory
VISLab

 

 

Contact Information

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:
CSE
ECE
ME
STAT
PSYC
ENTM
BIOL
BPSC
ECON
MATH
BIOENG
MGNT

Other Collaborators:
Keio University

Other Activities:
IEEE Biometrics Workshop 2014
IEEE Biometrics Workshop 2013
Worshop on DVSN 2009
Multibiometrics Book

Webmaster Contact Information:
Alex Shin
wshin@ece.ucr.edu

Last updated: July 1, 2017

 

 

Spatial Databases

Uncertain Spatial Data Handling: Modeling, Indexing and Query

Managing and manipulating uncertainty in spatial databases are important problems for various practical applications of geographic information systems. We present a probability-based method to model and index uncertain spatial data. In this scheme, each object is represented by a probability density function (PDF) and a general measure is proposed for measuring similarity between the objects. To index objects, an optimized Gaussian mixture hierarchy (OGMH) is designed to support both certain/uncertain data and certain/uncertain queries. As an example of uncertain query support OGMH is applied to the Mojave Desert endangered species protection real dataset. It is found that OGMH provides more selective, efficient and flexible search than the results provided by the existing trial and error approach for endangered species habitat search.

Handling Uncertain Spatial Data: Comparisons Between Indexing Structures

Managing and manipulating uncertainty in spatial databases are important problems for various practical applications. Unlike the traditional fuzzy approaches in relational databases, in this research we propose 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. We provide a comprehensive comparison among these three indices and plain R-tree 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.