Compressing Trajectory for Trajectory Indexing

Compressing Trajectory for Trajectory Indexing

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...

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Main Authors: Feng, Kaiyu, Shen, Zhiqi
Other Authors: Interdisciplinary Graduate School (IGS)
Format: Conference or Workshop Item
Language:English
Published: 2018
Subjects:
Trajectory
Trajectory Compressing
Online Access:https://hdl.handle.net/10356/88015
http://hdl.handle.net/10220/44524
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Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-88015
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spelling sg-ntu-dr.10356-880152020-11-01T04:43:57Z Compressing Trajectory for Trajectory Indexing Feng, Kaiyu Shen, Zhiqi Interdisciplinary Graduate School (IGS) Proceedings of the 2nd International Conference on Crowd Science and Engineering Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Trajectory Trajectory Compressing 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. NRF (Natl Research Foundation, S’pore) Accepted version 2018-03-07T05:05:17Z 2019-12-06T16:54:09Z 2018-03-07T05:05:17Z 2019-12-06T16:54:09Z 2017 Conference Paper Feng, K., & Shen, Z. (2017). Compressing Trajectory for Trajectory Indexing. Proceedings of the 2nd International Conference on Crowd Science and Engineering, 68-71. https://hdl.handle.net/10356/88015 http://hdl.handle.net/10220/44524 10.1145/3126973.3126979 en © 2017 Association for Computing Machinery (ACM). This is the author created version of a work that has been peer reviewed and accepted for publication by Proceedings of the 2nd International Conference on Crowd Science and Engineering, Association for Computing Machinery. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1145/3126973.3126979]. 4 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Trajectory
Trajectory Compressing
spellingShingle Trajectory
Trajectory Compressing
Feng, Kaiyu
Shen, Zhiqi
Compressing Trajectory for Trajectory Indexing
description 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.
author2 Interdisciplinary Graduate School (IGS)
author_facet Interdisciplinary Graduate School (IGS)
Feng, Kaiyu
Shen, Zhiqi
format Conference or Workshop Item
author Feng, Kaiyu
Shen, Zhiqi
author_sort Feng, Kaiyu
title Compressing Trajectory for Trajectory Indexing
title_short Compressing Trajectory for Trajectory Indexing
title_full Compressing Trajectory for Trajectory Indexing
title_fullStr Compressing Trajectory for Trajectory Indexing
title_full_unstemmed Compressing Trajectory for Trajectory Indexing
title_sort compressing trajectory for trajectory indexing
publishDate 2018
url https://hdl.handle.net/10356/88015
http://hdl.handle.net/10220/44524
_version_ 1683494484284801024

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