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

Comments

Degree granted by The University of Texas at Arlington

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