Doctoral Dissertations
Abstract
"Location Dependent Queries (LDQs) benefit from the rapid advances in communication and Global Positioning System (GPS) technologies to track moving objects' locations, and improve the quality-of-life by providing location relevant services and information to end users. The enormity of the underlying data maintained by LDQ applications - a large quantity of mobile objects and their frequent mobility - is, however, a major obstacle in providing effective and efficient services. Motivated by this obstacle, this thesis sets out in the quest to find improved methods to efficiently index, access, retrieve, and update volatile LDQ related mobile object data and information. Challenges and research issues are discussed in detail, and solutions are presented and examined."--Abstract, page iii.
Advisor(s)
Hurson, A. R.
Lin, Dan
Committee Member(s)
McMillin, Bruce M.
Madria, Sanjay Kumar
Sedigh, Sahra
Department(s)
Computer Science
Degree Name
Ph. D. in Computer Science
Publisher
Missouri University of Science and Technology
Publication Date
Spring 2015
Pagination
xii, 135 pages
Note about bibliography
Includes bibliographic references (pages 129-134).
Rights
© 2015 Lasanthi Nilmini Heendaliya, All rights reserved.
Document Type
Dissertation - Open Access
File Type
text
Language
English
Subject Headings
Intelligent transportation systems
Location-based services
Mobile computing
Information technology
Database management
Thesis Number
T 10716
Electronic OCLC #
913395639
Link to Catalog Record
Recommended Citation
Heendaliya, Lasanthi Nilmini, "Enabling near-term prediction of status for intelligent transportation systems: Management techniques for data on mobile objects" (2015). Doctoral Dissertations. 2386.
https://scholarsmine.mst.edu/doctoral_dissertations/2386
