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Big Data Mining and Analytics 2020, 3(2): 131-142 https://doi.org/10.26599/BDMA.2019.9020022
Open Access | Issue | Published: 27 February 2020

Online Real-Time Trajectory Analysis Based on Adaptive Time Interval Clustering Algorithm

Show Author's Information Jianjiang Li,  Huihui Jiao,  Jie Wang( ),  Zhiguo Liu,  Jie Wu
Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China.
TravelSkey Technology Ltd., Beijing 101318, China.
Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA.
Keywords:
data mining, storm, trajectory clustering, adaptive, density grid
Cite this article:
Li J, Jiao H, Wang J, et al. Online Real-Time Trajectory Analysis Based on Adaptive Time Interval Clustering Algorithm. Big Data Mining and Analytics, 2020, 3(2): 131-142. https://doi.org/10.26599/BDMA.2019.9020022
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Abstract Full text About this article

With the development of Chinese international trade, real-time processing systems based on ship trajectory have been used to cluster trajectory in real-time, so that the hot zone information of a sea ship can be discovered in real-time. This technology has great research value for the future planning of maritime traffic. However, ship navigation characteristics cannot be found in real-time with a ship Automatic Identification System (AIS) positioning system, and the clustering effect based on the density grid fixed-time-interval algorithm cannot resolve the shortcomings of real-time clustering. This study proposes an adaptive time interval clustering algorithm based on density grid (called DAC-Stream). This algorithm can perform adaptive time-interval clustering according to the size of the real-time ship trajectory data stream, so that a ship’s hot zone information can be found efficiently and in real-time. Experimental results show that the DAC-Stream algorithm improves the clustering effect and accelerates data processing compared with the fixed-time-interval clustering algorithm based on density grid (called DC-Stream).


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About this article

Online Real-Time Trajectory Analysis Based on Adaptive Time Interval Clustering Algorithm

Show Author's information Jianjiang Li, Huihui Jiao, Jie Wang( ), Zhiguo Liu, Jie Wu
Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China.
TravelSkey Technology Ltd., Beijing 101318, China.
Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA.

Abstract

With the development of Chinese international trade, real-time processing systems based on ship trajectory have been used to cluster trajectory in real-time, so that the hot zone information of a sea ship can be discovered in real-time. This technology has great research value for the future planning of maritime traffic. However, ship navigation characteristics cannot be found in real-time with a ship Automatic Identification System (AIS) positioning system, and the clustering effect based on the density grid fixed-time-interval algorithm cannot resolve the shortcomings of real-time clustering. This study proposes an adaptive time interval clustering algorithm based on density grid (called DAC-Stream). This algorithm can perform adaptive time-interval clustering according to the size of the real-time ship trajectory data stream, so that a ship’s hot zone information can be found efficiently and in real-time. Experimental results show that the DAC-Stream algorithm improves the clustering effect and accelerates data processing compared with the fixed-time-interval clustering algorithm based on density grid (called DC-Stream).

Keywords: data mining, storm, trajectory clustering, adaptive, density grid

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Publication history

Received: 08 October 2019
Accepted: 05 December 2019
Published: 27 February 2020
Issue date: June 2020

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© The author(s) 2020

Acknowledgements

This work was supported by the National Key R&D Program of China (No. 2017YFB0202104).

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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