"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.
Hurson, A. R.
McMillin, Bruce M.
Madria, Sanjay Kumar
Ph. D. in Computer Science
Missouri University of Science and Technology
xii, 135 pages
© 2015 Lasanthi Nilmini Heendaliya, All rights reserved.
Dissertation - Open Access
Library of Congress Subject Headings
Intelligent transportation systems
Electronic OCLC #
Link to Catalog Recordhttp://laurel.lso.missouri.edu/record=b10848353~S5
Heendaliya, Lasanthi Nilmini, "Enabling near-term prediction of status for intelligent transportation systems: Management techniques for data on mobile objects" (2015). Doctoral Dissertations. 2386.