Date of Award
Fall 12-2016
Degree Type
Dissertation
Degree Name
Ph.D.
Degree Program
Engineering and Applied Science
Department
Computer Science
Major Professor
Mahdi Abdelguerfi
Second Advisor
Elias Ioup
Third Advisor
Shengru Tu
Fourth Advisor
Christopher M. Summa
Fifth Advisor
Dimitrios Charalampidis
Abstract
Nowadays, many applications are continuously generating large-scale geospatial data. Vehicle GPS tracking data, aerial surveillance drones, LiDAR (Light Detection and Ranging), world-wide spatial networks, and high resolution optical or Synthetic Aperture Radar imagery data all generate a huge amount of geospatial data. However, as data collection increases our ability to process this large-scale geospatial data in a flexible fashion is still limited. We propose a framework for processing and analyzing large-scale geospatial and environmental data using a “Big Data” infrastructure. Existing Big Data solutions do not include a specific mechanism to analyze large-scale geospatial data. In this work, we extend HBase with Spatial Index(R-Tree) and HDFS to support geospatial data and demonstrate its analytical use with some common geospatial data types and data mining technology provided by the R language. The resulting framework has a robust capability to analyze large-scale geospatial data using spatial data mining and making its outputs available to end users.
Recommended Citation
Yang, Zhao, "Spatial Data Mining Analytical Environment for Large Scale Geospatial Data" (2016). University of New Orleans Theses and Dissertations. 2284.
https://scholarworks.uno.edu/td/2284
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Rights
The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.