Enabling near-term prediction of status for intelligent transportation systems: Management techniques for data on mobile objects

Author

Lasanthi Nilmini Heendaliya

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 systemsLocation-based servicesMobile computingInformation technologyDatabase management

Thesis Number

T 10716

Electronic OCLC #

913395639

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