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An efficient online direction-preserving compression approach for trajectory streaming data

Lookup NU author(s): Professor Raj Ranjan

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


Abstract

Online trajectory compression is an important method of efficiently managing massive volumes of trajectory streaming data. Current online trajectory methods generally do not preserve direction information and lack high computing performance for the fast compression. Aiming to solve these problems, this paper first proposed an online direction-preserving simplification method for trajectory streaming data, online DPTS by modifying an offline direction-preserving trajectory simplification (DPTS) method. We further proposed an optimized version of online DPTS called online DPTS+ by employing a data structure called bound quadrant system (BQS) to reduce the compression time of online DPTS. To provide a more efficient solution to reduce compression time, this paper explored the feasibility of using contemporary general-purpose computing on a graphics processing unit (GPU). The GPU-aided approach paralleled the major computing part of online DPTS+ that is the SP-theo algorithm. The results show that by maintaining a comparable compression error and compression rate, (1) the online DPTS outperform offline DPTS with up to 21% compression time, (2) the compression time of online DPTS+ algorithm is 3.95 times faster than that of online OPTS, and (3) the GPU-aided method can significantly reduce the time for graph construction and for finding the shortest path with a speedup of 31.4 and 7.88 (on average), respectively. The current approach provides a new tool for fast online trajectory streaming data compression. (C) 2016 Elsevier B.V. All rights reserved.


Publication metadata

Author(s): Deng Z, Han W, Wang LZ, Ranjan R, Zomaya AY, Jie W

Publication type: Article

Publication status: Published

Journal: Future Generation Computer Systems

Year: 2017

Volume: 68

Pages: 150-162

Print publication date: 01/03/2017

Online publication date: 04/10/2016

Acceptance date: 29/09/2016

Date deposited: 20/08/2017

ISSN (print): 0167-739X

ISSN (electronic): 1872-7115

Publisher: Elsevier

URL: https://doi.org/10.1016/j.future.2016.09.019

DOI: 10.1016/j.future.2016.09.019


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