Querying continuous recurrent convoys of interest
Publication Type
Conference Proceeding Article
Publication Date
11-2019
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.
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2019
First Page
436
Last Page
439
ISBN
9781450369091
Identifier
10.1145/3347146.3359083
City or Country
Chicago, IL, USA
Citation
YADAMJAV, Munkh-Erdene; BAO, Zhifeng; CHOUDURY, Farhana Murtaza; SAMET, Hanan; and ZHENG, Baihua.
Querying continuous recurrent convoys of interest. (2019). Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2019. 436-439.
Available at: https://ink.library.smu.edu.sg/sis_research/4671
Additional URL
http://doi.org/10.1145/3347146.3359083