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Management and Prediction of Moving ...

Islam, Abdullah.

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Management and Prediction of Moving Objects under Location Uncertainty.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Management and Prediction of Moving Objects under Location Uncertainty./
Author:	Islam, Abdullah.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	40 p.
Notes:	Source: Masters Abstracts International, Volume: 81-10.
Contained By:	Masters Abstracts International81-10.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27744614
ISBN:	9798641789934

Management and Prediction of Moving Objects under Location Uncertainty.
Islam, Abdullah.

Management and Prediction of Moving Objects under Location Uncertainty. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 40 p.

Source: Masters Abstracts International, Volume: 81-10.

Thesis (Master's)--University of Washington, 2020.

This item must not be sold to any third party vendors.

In spatio-temporal systems, precise location data is desirable but often not available due to obfuscation, privacy, hardware inaccuracies, and other factors. Progress has been made in research which deals with the uncertainty of moving objects' location data. However, much of the existing work does not always consider factors such as constraints imposed by the topology of road networks, and harmonic integration between past movements, current, and prospective imprecise positions. In this thesis, we propose an approach that utilizes time, distance, and connectivity constraints of a road network to infer a moving object's past, present, and future locations more precisely when its exact location data is not available. The experimental results using real GPS trajectories confirm the efficiency of our proposed solution for reducing uncertainty and inferring historical, and future locations.

ISBN: 9798641789934Subjects--Topical Terms:

523869
Computer science.

Management and Prediction of Moving Objects under Location Uncertainty.
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245 1 0 $a Management and Prediction of Moving Objects under Location Uncertainty.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 40 p.
500 $a Source: Masters Abstracts International, Volume: 81-10.
500 $a Advisor: Ali, Mohamed H;Hendawi, Abdeltawab.
502 $a Thesis (Master's)--University of Washington, 2020.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a In spatio-temporal systems, precise location data is desirable but often not available due to obfuscation, privacy, hardware inaccuracies, and other factors. Progress has been made in research which deals with the uncertainty of moving objects' location data. However, much of the existing work does not always consider factors such as constraints imposed by the topology of road networks, and harmonic integration between past movements, current, and prospective imprecise positions. In this thesis, we propose an approach that utilizes time, distance, and connectivity constraints of a road network to infer a moving object's past, present, and future locations more precisely when its exact location data is not available. The experimental results using real GPS trajectories confirm the efficiency of our proposed solution for reducing uncertainty and inferring historical, and future locations.
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