With the establishment of Automatic Identification System(AIS) networks, maritime vessel trajectories are becoming increasingly available. The visualization of AIS trajectories data is an effective way on large scale spatiotemporal data analysis and is critical for real-time applications ranging from military surveillance to transportation management. In this paper we use the real AIS data as experimental data and uses Hadoop as data processing and storage, presents a dynamic visualization of global marine AIS data and local sea area situation. A multi charts visualization model is presented, where the characteristics of complex ships in the port area can be analyzed.
- large data,
Maritime transportation is the most important transportation nowadays. There are a vast number of ports, ships, and countless sensors in different data formats, producing huge amounts of data each minute. How to use these data reliably, accurately, and efficiently, and how to effectively discover knowledge from spatiotemporal large data are hot issues in the related area . Data visualization is the visual representation of data. By designing a reasonable mapping scheme for abstract data, the data can be more easily understood, and the hidden information and rules can be displayed to users efficiently and accurately . Efficient visualization scheme plays an important role in analyzing and interpreting data and improves the accessibility, comprehensibility, and usability of complex data . Through the spatial and temporal statistical analysis of global AIS data on the large data platform, and visualization of data display and analysis, we can get a lot of valuable information, which can be used for surveillance and command and control in maritime traffic systems .
In this paper we present a visualization approach on AIS data, including a dynamic visualization and a multi-charts visualization model, Hadoop is used as data processing and storage, and experiments on real AIS data is made, which shows the effectiveness and correctness of our approach.
The AIS data are very large location data sets, the data items on a single day can be millions, so the calculation on marine traffic data should use the big data frameworks such as Hadoop or Spark, but these traditional big data structures are ill-equipped in supporting spatial data as it deals with spatial data in the same way as non-spatial data. The programs defined through the map and reduce cannot access the constructed spatial index. Nevertheless, some applications built there own platform for AIS data storage and visualization, including Marine Traffic , Vessel Finder , AISHub. While some similar applications existed in China, such as ChuanXun, Sichuan. These are AIS data processing platforms, with the help of AIS big data, provide people with ship location information visualization or data service such as fleet tracking, historical data reproduction, navigation forecasting, port statistics, etc. Trajectory visualization of AIS Spatio-temporal data in these related works mainly uses expanded maps, arrows, and a series of maps at different times to show the changing process of trajectory, so that the observer can form a sense of dynamic change.
With the rapid development of related research, AIS data analysis provides a decision-making basis for various military, economy, and other applications. At present, combining human visual perception with computer processing ability, big data visualization is an important approach to study this problem. In this paper we use the real AIS data as experimental data and uses Hadoop as data processing and storage, presents a dynamic visualization of global marine AIS data and local sea area situation. A multi-graph visualization model is presented, where the characteristics of complex ships in the port area can be analyzed. Future works should be focused on 1.More sophisticated data pre-processing approaches in data cleaning. 2.Detailed analysis and interpretation of visualization results. More visualizations models related to real applications, such as outline detection, clustering.etc.
The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.
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FULL Paper PDF file:The Data Visualization of Large Scale AIS Trajectories Data on Hadoop
The Data Visualization of Large Scale AIS Trajectories Data on Hadoop
2020 6th International Conference on Control, Automation, and Robotics (ICCAR), Singapore, Singapore, 2020, pp. 758-761,
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Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.