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Compressing large scale urban trajectory data

Published: 13 April 2014 Publication History

Abstract

With the increasing size of trajectory data generated by location-based services and applications which are built from inexpensive GPS-enabled devices in urban environments, the need for compressing large scale trajectories becomes obvious. This paper proposes a scalable urban trajectory compression scheme (SUTC) that can compress a set of trajectories collectively by exploiting common movement behaviors among the urban moving objects such as vehicles and smartphone users. SUTC exploits that urban objects moving in similar behaviors naturally, especially large-scale of human and vehicle which are moving constrained by some geographic context (e.g., road networks or routes). To exploit redundancy across a large set of trajectories, SUTC first transforms trajectory sequences from Euclidean space to network-constrained space and represents each trajectory with a sequence of symbolic positions in textual domain. Then, SUTC performs compression by encoding the symbolic sequences with general-purpose compression methods. The key challenge in this process is how to transform the trajectory data from spatio-temporal domain to textual domain without introducing unbounded error. We develop two strategies (i.e., velocity-based symbolization, and beacon-based symbolization) to enrich the symbol sequences and achieves high compression ratios by sacrificing a little bit the decoding accuracy. Besides, we also optimize the organization of trajectory data in order to adapt it to practical compression algorithms, and increase the efficiency of compressing processes. Our experiments on real large-scale trajectory datasets demonstrate the superiority and feasibility of the our proposed algorithms.

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cover image ACM Conferences
CloudDP '14: Proceedings of the Fourth International Workshop on Cloud Data and Platforms
April 2014
41 pages
ISBN:9781450327145
DOI:10.1145/2592784
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 April 2014

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Author Tags

  1. data compression
  2. spatio-temporal trajectory data

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EuroSys 2014
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EuroSys 2014: Ninth Eurosys Conference 2014
April 13, 2014
Amsterdam, The Netherlands

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CloudDP '14 Paper Acceptance Rate 6 of 16 submissions, 38%;
Overall Acceptance Rate 6 of 16 submissions, 38%

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Cited By

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  • (2022)SaveDat: Spatio-Temporal Trajectory Compression by LSTM2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)10.1109/ICITE56321.2022.10101477(442-450)Online publication date: 11-Nov-2022
  • (2022)An intelligent linear time trajectory data compression framework for smart planning of sustainable metropolitan citiesTransactions on Emerging Telecommunications Technologies10.1002/ett.388633:2Online publication date: 14-Feb-2022
  • (2021)Network-based Trajectory Search over Time IntervalsBig Data Research10.1016/j.bdr.2021.100221(100221)Online publication date: Feb-2021
  • (2021)A Semantics-Based Trajectory Segmentation Simplification MethodJournal of Geovisualization and Spatial Analysis10.1007/s41651-021-00088-55:2Online publication date: 27-Sep-2021
  • (2020)A Multi-UAVs’ Trajectory data Compression Method Based on 3D-SPM Algorithm2020 39th Chinese Control Conference (CCC)10.23919/CCC50068.2020.9188961(6874-6880)Online publication date: Jul-2020
  • (2019)Sunshine-Based Trajectory SimplificationIEEE Access10.1109/ACCESS.2019.2907312(1-1)Online publication date: 2019
  • (2019)SunChoice: A Novel Framework for Sunshine-Based Trajectories AnalysisIEEE Access10.1109/ACCESS.2019.29071927(41757-41769)Online publication date: 2019
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  • (2019)Self-Adaptive Online Trajectory Sampling (SAOTS) Using Spectral Domain PropertiesTransportation Research Procedia10.1016/j.trpro.2019.05.04538(874-893)Online publication date: 2019
  • (2019)Spatio-temporal top-k term search over sliding windowWorld Wide Web10.1007/s11280-018-0606-x22:5(1953-1970)Online publication date: 1-Sep-2019
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