Compressing Trajectory for Trajectory Indexing
Date of Issue2017
Proceedings of the 2nd International Conference on Crowd Science and Engineering
Interdisciplinary Graduate School
Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly
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.
© 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].