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).
Highlights
With the development of science and technology, we have entered the era of big data[1,2], and data is of increasing significance to the development of the entire society
A ship Automatic Identification System (AIS) positioning system cannot find the navigation characteristics of a ship in real-time, the problem of using a fixed-time-interval clustering effect is critical to trajectory data flow velocity fluctuation based on the Storm[18,19]
This paper mainly completes the following work: (1) An adaptive time interval clustering algorithm based on density grid and a distributed clustering framework flow are proposed on the basis of the realtime processing framework of Storm big data to examine the characteristics of streaming trajectory data
Summary
With the development of science and technology, we have entered the era of big data[1,2], and data is of increasing significance to the development of the entire society. Another study proposed an adaptive kernel density-based method for the motion pattern mining of AIS trajectory data. A ship AIS positioning system cannot find the navigation characteristics of a ship in real-time, the problem of using a fixed-time-interval clustering effect is critical to trajectory data flow velocity fluctuation based on the Storm[18,19]. (1) An adaptive time interval clustering algorithm based on density grid and a distributed clustering framework flow are proposed on the basis of the realtime processing framework of Storm big data to examine the characteristics of streaming trajectory data. The actual test results show that the proposed adaptive time interval clustering algorithm based on density grid (called DAC-Stream) algorithm improves the clustering effect, and accelerates data processing
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