Publication Type
Journal Article
Version
acceptedVersion
Publication Date
9-2020
Abstract
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.
Keywords
Theory of computation, Data structures and algorithms for data management, Information systems, Data stream mining
Discipline
Databases and Information Systems | Data Storage Systems | Theory and Algorithms
Research Areas
Data Science and Engineering
Publication
ACM Transactions on Intelligent Systems and Technology
Volume
11
Issue
5
First Page
59:1
Last Page
24
ISSN
2157-6904
Identifier
10.1145/3400730
Publisher
ACM
Citation
YADAMJAV, Munkh-Erdene; BAO, Zhifeng; ZHENG, Baihua; CHOUDHURY, Farhana M.; and SAMET, Hanan.
Querying recurrent convoys over trajectory data. (2020). ACM Transactions on Intelligent Systems and Technology. 11, (5), 59:1-24.
Available at: https://ink.library.smu.edu.sg/sis_research/5277
Copyright Owner and License
Authors / LARC
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3400730
Included in
Databases and Information Systems Commons, Data Storage Systems Commons, Theory and Algorithms Commons