Graduation Semester and Year
2018
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Computer Engineering
Department
Computer Science and Engineering
First Advisor
Ramez Elmasri
Abstract
The emergence and presence of satellites and GPS devices have led to the creation of a huge amounts of spatial and spatio-temporal data, which had significant effects on creating new applications to analyze and mine these data. In this regard, a lot of research has been done on moving objects databases as a part of spatial and spatio-temporal databases. In this dissertation, we focus on those moving objects that are not allowed to move in all directions freely, but they (almost) always are restricted to travel on a specific network. One of the most popular example of these moving objects are vehicles that are supposed to travel on a Road Network. This kind of databases are called Network-constrained (or Fixed-network) Moving Object databases. First, we formalize Network-constrained Moving Object databases, and we come up with a Data Model, Data Schema, and Query Schema. Then, we introduce a data structure to index these kinds of databases. We also present a Traffic Congestion Prediction tool by using Deep Artificial Neural Network. Map Integration, Map Matching, Map Integrity are other applications in this area that we consider in this dissertation.
Keywords
GIS, Data model, Indexing, Map matching, Frechet distance, Network-constrained moving objects, BerlinMOD
Disciplines
Computer Sciences | Physical Sciences and Mathematics
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Fouladgar, Mohammadhani, "Indexing, Querying, Prediction, and Integration for Network-constrained Moving Objects Databases" (2018). Computer Science and Engineering Dissertations. 377.
https://mavmatrix.uta.edu/cse_dissertations/377
Comments
Degree granted by The University of Texas at Arlington