TY - GEN
T1 - Compressing trajectory for trajectory indexing
AU - Feng, Kaiyu
AU - Shen, Zhiqi
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/7/6
Y1 - 2017/7/6
N2 - Nowadays, as many devices like mobile phones and smart watch/band are equipped with GPS-devices, a large volume of trajectory data is generated every day. With the availability of such trajectory data, many mining tasks have been proposed and investigated in the past decade. Since the raw trajectory data is usually very large, it is a big challenge to analyse and mine the raw data directly. In order to address this issue, a branch of research has been done to compress the trajectory data. This paper surveys recent research about trajectory compression. An overview of existing techniques for trajectory compression is provided.
AB - Nowadays, as many devices like mobile phones and smart watch/band are equipped with GPS-devices, a large volume of trajectory data is generated every day. With the availability of such trajectory data, many mining tasks have been proposed and investigated in the past decade. Since the raw trajectory data is usually very large, it is a big challenge to analyse and mine the raw data directly. In order to address this issue, a branch of research has been done to compress the trajectory data. This paper surveys recent research about trajectory compression. An overview of existing techniques for trajectory compression is provided.
KW - Survey
KW - Trajectory
KW - Trajectory Compressing
UR - http://www.scopus.com/inward/record.url?scp=85030461602&partnerID=8YFLogxK
U2 - 10.1145/3126973.3126979
DO - 10.1145/3126973.3126979
M3 - Conference contribution
AN - SCOPUS:85030461602
T3 - ACM International Conference Proceeding Series
SP - 68
EP - 71
BT - Proceedings of 2017 2nd International Conference on Crowd Science and Engineering, ICCSE 2017
PB - Association for Computing Machinery
T2 - 2nd International Conference on Crowd Science and Engineering, ICCSE 2017
Y2 - 6 July 2017 through 9 July 2017
ER -