Querying continuous recurrent convoys of interest

Moving objects equipped with location-positioning devices continuously generate a large amount of spatio-temporal trajectory data. An interesting finding over a trajectory stream is a group of objects that are travelling together for a certain period of time. Existing studies on mining co-moving obj...

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Main Authors: YADAMJAV, Munkh-Erdene, BAO, Zhifeng, CHOUDURY, Farhana Murtaza, SAMET, Hanan, ZHENG, Baihua
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Language:English
Published: Institutional Knowledge at Singapore Management University 2019
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Online Access:https://ink.library.smu.edu.sg/sis_research/4671
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spelling sg-smu-ink.sis_research-56742020-01-02T06:18:03Z Querying continuous recurrent convoys of interest YADAMJAV, Munkh-Erdene BAO, Zhifeng CHOUDURY, Farhana Murtaza SAMET, Hanan ZHENG, Baihua Moving objects equipped with location-positioning devices continuously generate a large amount of spatio-temporal trajectory data. An interesting finding over a trajectory stream is a group of objects that are travelling together for a certain period of time. Existing studies on mining co-moving objects do not consider an important correlation between co-moving objects, which is the reoccurrence of the movement pattern. In this study, we define a problem of finding recurrent pattern of co-moving objects from streaming trajectories and propose an efficient solution that enables us to discover recent co-moving object patterns repeated within a given time period. Experimental results on a real-life trajectory database show the efficiency of our method. 2019-11-05T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/4671 info:doi/10.1145/3347146.3359083 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
spellingShingle Databases and Information Systems
YADAMJAV, Munkh-Erdene
BAO, Zhifeng
CHOUDURY, Farhana Murtaza
SAMET, Hanan
ZHENG, Baihua
Querying continuous recurrent convoys of interest
description Moving objects equipped with location-positioning devices continuously generate a large amount of spatio-temporal trajectory data. An interesting finding over a trajectory stream is a group of objects that are travelling together for a certain period of time. Existing studies on mining co-moving objects do not consider an important correlation between co-moving objects, which is the reoccurrence of the movement pattern. In this study, we define a problem of finding recurrent pattern of co-moving objects from streaming trajectories and propose an efficient solution that enables us to discover recent co-moving object patterns repeated within a given time period. Experimental results on a real-life trajectory database show the efficiency of our method.
format text
author YADAMJAV, Munkh-Erdene
BAO, Zhifeng
CHOUDURY, Farhana Murtaza
SAMET, Hanan
ZHENG, Baihua
author_facet YADAMJAV, Munkh-Erdene
BAO, Zhifeng
CHOUDURY, Farhana Murtaza
SAMET, Hanan
ZHENG, Baihua
author_sort YADAMJAV, Munkh-Erdene
title Querying continuous recurrent convoys of interest
title_short Querying continuous recurrent convoys of interest
title_full Querying continuous recurrent convoys of interest
title_fullStr Querying continuous recurrent convoys of interest
title_full_unstemmed Querying continuous recurrent convoys of interest
title_sort querying continuous recurrent convoys of interest
publisher Institutional Knowledge at Singapore Management University
publishDate 2019
url https://ink.library.smu.edu.sg/sis_research/4671
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