Benchmark analysis demonstrates accelerated K-means trajectory clustering for massive maritime AIS data on Spark clusters, indicating improved scalability for vessel traffic monitoring.
With the rapid development of water transportation, ship transportation has attracted more and more attention. As the number of ships continues to increase, the amount of AIS data continues to grow, and there are deficiencies such as data complexity and difficulty in processing. In order to analyze the massive AIS data and fully mine effective information. This paper builds an AIS data processing and analysis platform based on Spark, Hadoop, and Mesos, uses the KMEANS algorithm to perform cluster analysis on ship data, and obtains clearer ship trajectory information, and compares the time required for cluster analysis of Spark clusters and no installation A comparative analysis of the time required for cluster analysis by the Spark computer. From the experimental results, it can be seen that the speed of cluster analysis of AIS ship data points in the Spark cluster is greatly improved compared with non-big data platforms, which can improve the data processing effectiveness.
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Chu et al. (2020) studied this question.
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