Compressing trajectory for trajectory indexing

Kaiyu Feng; Zhiqi Shen

doi:10.1145/3126973.3126979

Compressing trajectory for trajectory indexing

Kaiyu Feng, Zhiqi Shen

Nanyang Technological University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	Proceedings of 2017 2nd International Conference on Crowd Science and Engineering, ICCSE 2017
Publisher	Association for Computing Machinery
Pages	68-71
Number of pages	4
ISBN (Electronic)	9781450353755
DOIs	https://doi.org/10.1145/3126973.3126979
Publication status	Published - 6 Jul 2017
Externally published	Yes
Event	2nd International Conference on Crowd Science and Engineering, ICCSE 2017 - Beijing, China Duration: 6 Jul 2017 → 9 Jul 2017

Publication series

Name	ACM International Conference Proceeding Series
Volume	Part F130655

Conference

Conference	2nd International Conference on Crowd Science and Engineering, ICCSE 2017
Country/Territory	China
City	Beijing
Period	6/07/17 → 9/07/17

Keywords

Survey
Trajectory
Trajectory Compressing

Access to Document

10.1145/3126973.3126979

Cite this

@inproceedings{4fd4968ff5754b7cac21216e5373465c,

title = "Compressing trajectory for trajectory indexing",

abstract = "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.",

keywords = "Survey, Trajectory, Trajectory Compressing",

author = "Kaiyu Feng and Zhiqi Shen",

note = "Publisher Copyright: {\textcopyright} 2017 Association for Computing Machinery.; 2nd International Conference on Crowd Science and Engineering, ICCSE 2017 ; Conference date: 06-07-2017 Through 09-07-2017",

year = "2017",

month = jul,

day = "6",

doi = "10.1145/3126973.3126979",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "68--71",

booktitle = "Proceedings of 2017 2nd International Conference on Crowd Science and Engineering, ICCSE 2017",

}

Feng, K & Shen, Z 2017, Compressing trajectory for trajectory indexing. in Proceedings of 2017 2nd International Conference on Crowd Science and Engineering, ICCSE 2017. ACM International Conference Proceeding Series, vol. Part F130655, Association for Computing Machinery, pp. 68-71, 2nd International Conference on Crowd Science and Engineering, ICCSE 2017, Beijing, China, 6/07/17. https://doi.org/10.1145/3126973.3126979

Compressing trajectory for trajectory indexing. / Feng, Kaiyu; Shen, Zhiqi.
Proceedings of 2017 2nd International Conference on Crowd Science and Engineering, ICCSE 2017. Association for Computing Machinery, 2017. p. 68-71 (ACM International Conference Proceeding Series; Vol. Part F130655).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Compressing trajectory for trajectory indexing

AU - Feng, Kaiyu

AU - Shen, Zhiqi

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

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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

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T2 - 2nd International Conference on Crowd Science and Engineering, ICCSE 2017

Y2 - 6 July 2017 through 9 July 2017

ER -

Compressing trajectory for trajectory indexing

Abstract

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Keywords

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