Date of Degree


Document Type


Degree Name



Computer Science


Jianting Zhang

Subject Categories

Databases and Information Systems


Big Data, Spatial Join, Spatial Index, Parallel Computing


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.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.