Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88015
Title: Compressing Trajectory for Trajectory Indexing
Authors: Feng, Kaiyu
Shen, Zhiqi
Keywords: Trajectory
Trajectory Compressing
Issue Date: 2017
Source: Feng, K., & Shen, Z. (2017). Compressing Trajectory for Trajectory Indexing. Proceedings of the 2nd International Conference on Crowd Science and Engineering, 68-71.
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.
URI: https://hdl.handle.net/10356/88015
http://hdl.handle.net/10220/44524
DOI: 10.1145/3126973.3126979
Rights: © 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].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:IGS Conference Papers

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