Open Access System for Information Sharing

Login Library

 

Thesis
Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Mining Algorithms for Network-Constrained Trajectories

Title
Mining Algorithms for Network-Constrained Trajectories
Authors
노국필
Date Issued
2012
Publisher
포항공과대학교
Abstract
With the advent of ubiquitous computing, a massive amount of trajectory data has been published and shared in many websites. Such computing also motivates the needs of the online mining of such data, to fit user-specific preferences or context (\eg, time of the day). While many trajectory analysis algorithms have been proposed, they typically do not consider the restrictions of the underlying road network and have focused on a spatio-temporal query. This dissertation discusses desirable properties for mining the road network trajectories. As the existing work does not fully satisfy these properties, we develop (1) trajectory representation and (2) distance measure that satisfy all the desirable properties we identified. Based on the representation and distance measure, we discuss how to efficiently evaluate similarity search queries which include three types of similarity semantics-- whole, subpattern, and reverse subpattern. With the distance measure that reflects the spatial proximity of the road network trajectories, we develop efficient clustering algorithms that reduce the number of distance computations during the clustering process. Moreover, we devise a clustering algorithm considering selection conditions representing user contexts to fit user-specific preferences or context (\eg, time of the day). Our experimental results demonstrate the efficiency and effectiveness of our proposed method using real-life trajectory data.
URI
http://postech.dcollection.net/jsp/common/DcLoOrgPer.jsp?sItemId=000001388678
http://oasis.postech.ac.kr/handle/2014.oak/1645
Article Type
Thesis
Files in This Item:
There are no files associated with this item.

qr_code

  • mendeley

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Views & Downloads

Browse