You are not logged in.
Openly accessible

DoSTra: Discovering common behaviors of objects using the duration of staying on each location of trajectories

Guo, Limin, Huang, Guangyan, Gao, Xu, He, Jing, Wu, Bin and Guo, Haoming 2015, DoSTra: Discovering common behaviors of objects using the duration of staying on each location of trajectories, in AAAI 2015 : Proceedings from the AAAI Conference on Artificial Intelligence Workshop, Association for the Advancement of Artificial Intelligence (AAAI), Palo Alto, Calif., pp. 9-17.

Attached Files
Name Description MIMEType Size Downloads
huang-dostradiscovering-2015.pdf Published version application/pdf 1.59MB 2

Title DoSTra: Discovering common behaviors of objects using the duration of staying on each location of trajectories
Author(s) Guo, Limin
Huang, GuangyanORCID iD for Huang, Guangyan orcid.org/0000-0002-1821-8644
Gao, Xu
He, Jing
Wu, Bin
Guo, Haoming
Conference name AAAI Conference on Artificial Intelligence: Workshop 15-14 (29th : 2015 : Austin, Texas)
Conference location Austin, Texas
Conference dates 25-30 Jan. 2015
Title of proceedings AAAI 2015 : Proceedings from the AAAI Conference on Artificial Intelligence Workshop
Publication date 2015
Start page 9
End page 17
Total pages 9
Publisher Association for the Advancement of Artificial Intelligence (AAAI)
Place of publication Palo Alto, Calif.
Summary Since semantic trajectories can discover more semantic meanings of a user's interests without geographic restrictions, research on semantic trajectories has attracted a lot of attentions in recent years. Most existing work discover the similar behavior of moving objects through analysis of their semantic trajectory pattern, that is, sequences of locations. However, this kind of trajectories without considering the duration of staying on a location limits wild applications. For example, Tom and Anne have a common pattern of Home→Restaurant → Company → Restaurant, but they are not similar, since Tom works at Restaurant, sends snack to someone at Company and return to Restaurant while Anne has breakfast at Restaurant, works at Company and has lunch at Restaurant. If we consider duration of staying on each location we can easily to differentiate their behaviors. In this paper, we propose a novel approach for discovering common behaviors by considering the duration of staying on each location of trajectories (DoSTra). Our approach can be used to detect the group that has similar lifestyle, habit or behavior patterns and predict the future locations of moving objects. We evaluate the experiment based on synthetic dataset, which demonstrates the high effectiveness and efficiency of the proposed method.
ISBN 9781577357254
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 810105 Intelligence
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, Association for the Advancement of Artificial Intelligence
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083542

Document type: Conference Paper
Collections: School of Information Technology
Open Access Collection
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 1 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 145 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Thu, 02 Jun 2016, 10:36:17 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.