Dissertations, Theses, and Capstone Projects
Date of Degree
2-2016
Document Type
Dissertation
Degree Name
Ph.D.
Program
Computer Science
Advisor
Jianting Zhang
Subject Categories
Databases and Information Systems
Keywords
Big Data, Spatial Join, Spatial Index, Parallel Computing
Abstract
Rapidly growing volume of spatial data has made it desirable to develop efficient techniques for managing large-scale spatial data. Traditional spatial data management techniques cannot meet requirements of efficiency and scalability for large-scale spatial data processing. In this dissertation, we have developed new data-parallel designs for large-scale spatial data management that can better utilize modern inexpensive commodity parallel and distributed platforms, including multi-core CPUs, many-core GPUs and computer clusters, to achieve both efficiency and scalability. After introducing background on spatial data management and modern parallel and distributed systems, we present our parallel designs for spatial indexing and spatial join query processing on both multi-core CPUs and GPUs for high efficiency as well as their integrations with Big Data systems for better scalability. Experiment results using real world datasets demonstrate the effectiveness and efficiency of the proposed techniques on managing large-scale spatial data.
Recommended Citation
You, Simin, "Large-Scale Spatial Data Management on Modern Parallel and Distributed Platforms" (2016). CUNY Academic Works.
https://academicworks.cuny.edu/gc_etds/673