SparseTrajAnalytics: an Interactive Visual Analytics System for Sparse Trajectory Data

Ye, Xinyue; Du, Jiaxin; Gong, Xi; Zhao, Ye; Al-Dohuki, Shamal; Kamw, Farah

doi:10.1007/s41651-020-00068-1

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…The main tasks of STDMAS include managing the storage of ship trajectory data from various sources to provide protection service for the historical data on spatial and temporal ship trajectory; assessing and analyzing the quality of trajectory data from various sources to provide the data support for evaluating the equipment performance based on the collected ship target trajectory data; and analyzing and mining the information on the hot spots and situation regularity in the waters around China to provide the technical support for the analysis and decision making in maritime supervision and management [ 5 , 6 ]. It can also analyze the navigational characteristics, pattern, and regularity of ships to overcome the impossible identification of many targets at sea and provide the basis for maritime administration and military commanders in the selection and tracking of key targets among many unknown ships [ 7 ].…”

Section: Demand and Functional Analysismentioning

confidence: 99%

The Design and Development of a Ship Trajectory Data Management and Analysis System Based on AIS

Feng

Bing

Luo

et al. 2021

Sensors

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To address the data storage, management, analysis, and mining of ship targets, the object-oriented method was employed to design the overall structure and functional modules of a ship trajectory data management and analysis system (STDMAS). This paper elaborates the detailed design and technical information of the system’s logical structure, module composition, physical deployment, and main functional modules such as database management, trajectory analysis, trajectory mining, and situation analysis. A ship identification method based on the motion features was put forward. With the method, ship trajectory was first partitioned into sub-trajectories in various behavioral patterns, and effective motion features were then extracted. Machine learning algorithms were utilized for training and testing to identify many types of ships. STDMAS implements such functions as database management, trajectory analysis, historical situation review, and ship identification and outlier detection based on trajectory classification. STDMAS can satisfy the practical needs for the data management, analysis, and mining of maritime targets because it is easy to apply, maintain, and expand.

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Section: Demand and Functional Analysismentioning

confidence: 99%