Querying recurrent convoys over trajectory data

• Main Authors: YADAMJAV, Munkh-Erdene, BAO, Zhifeng, ZHENG, Baihua, CHOUDHURY, Farhana M., SAMET, Hanan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Theory of computation; Data structures and algorithms for data management; Information systems; Data stream mining; Databases and Information Systems; Data Storage Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/5277; https://ink.library.smu.edu.sg/context/sis_research/article/6280/viewcontent/TIST20_Final.pdf
• Institution: Singapore Management University

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.