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• W4315926614 abstract "Automatic Identification System (AIS) data contains static and dynamic information for identification, tracking, and collision avoidance of vessels, as well as management of maritime activities. In the utilization of AIS data, a data pipeline framework is needed which includes the process of capturing messages, translating messages, detecting data corruption, cleaning damaged data, reconstructing trajectories, visualizing data, and storing data for analysis needs. Usually, the analysis and storage of AIS data are done offline which makes it not conducive to understanding vessel dynamics. In addition, large AIS data contains incomplete and noisy information that affects the quality of the trajectory. In this context, this article has proposed an AIS data pipeline framework in the form of pre-processing and real-time trajectory reconstruction. Pre-processing is intended to eliminate data anomalies caused by transmission errors. While the trajectory reconstruction process to overcome missing values due to noise is based on the Cubic Spline interpolation technique. All processes in the data pipeline framework are streamed using Apache Kafka. The simulation results show that the proposed AIS data pipelines framework has succeeded in visualizing the data flow in the form of a map application and trajectory reconstruction in real-time." @default.
• W4315926614 created "2023-01-14" @default.
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• W4315926614 date "2022-12-08" @default.
• W4315926614 modified "2023-09-27" @default.
• W4315926614 title "Data Pipeline Framework for AIS Data Processing" @default.
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• W4315926614 doi "https://doi.org/10.1109/icic56845.2022.10006941" @default.
• W4315926614 hasPublicationYear "2022" @default.
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