Online Real-Time Trajectory Analysis Based on Adaptive Time Interval Clustering Algorithm
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 plannin...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2020-06-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2019.9020022 |
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| _version_ | 1832572826019692544 |
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| author | Jianjiang Li Huihui Jiao Jie Wang Zhiguo Liu Jie Wu |
| author_facet | Jianjiang Li Huihui Jiao Jie Wang Zhiguo Liu Jie Wu |
| author_sort | Jianjiang Li |
| collection | DOAJ |
| description | 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). |
| format | Article |
| id | doaj-art-9e30f40de7544bcc99e1180c13d65921 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2020-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-9e30f40de7544bcc99e1180c13d659212025-02-02T06:50:33ZengTsinghua University PressBig Data Mining and Analytics2096-06542020-06-013213114210.26599/BDMA.2019.9020022Online Real-Time Trajectory Analysis Based on Adaptive Time Interval Clustering AlgorithmJianjiang Li0Huihui Jiao1Jie Wang2Zhiguo Liu3Jie Wu4<institution content-type="dept">Department of Computer Science and Technology</institution>, <institution>University of Science and Technology Beijing</institution>, <city>Beijing</city> <postal-code>100083</postal-code>, <country>China</country>.<institution content-type="dept">Department of Computer Science and Technology</institution>, <institution>University of Science and Technology Beijing</institution>, <city>Beijing</city> <postal-code>100083</postal-code>, <country>China</country>.<institution>TravelSkey Technology Ltd.</institution>, <city>Beijing</city> <postal-code>101318</postal-code>, <country>China</country>.<institution content-type="dept">Department of Computer Science and Technology</institution>, <institution>University of Science and Technology Beijing</institution>, <city>Beijing</city> <postal-code>100083</postal-code>, <country>China</country>.<institution content-type="dept">Department of Computer and Information Sciences</institution>, <institution>Temple University</institution>, <city>Philadelphia</city>, <state>PA</state> <postal-code>19122</postal-code>, <country>USA</country>.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).https://www.sciopen.com/article/10.26599/BDMA.2019.9020022stormtrajectory clusteringadaptivedata miningdensity grid |
| spellingShingle | Jianjiang Li Huihui Jiao Jie Wang Zhiguo Liu Jie Wu Online Real-Time Trajectory Analysis Based on Adaptive Time Interval Clustering Algorithm Big Data Mining and Analytics storm trajectory clustering adaptive data mining density grid |
| title | Online Real-Time Trajectory Analysis Based on Adaptive Time Interval Clustering Algorithm |
| title_full | Online Real-Time Trajectory Analysis Based on Adaptive Time Interval Clustering Algorithm |
| title_fullStr | Online Real-Time Trajectory Analysis Based on Adaptive Time Interval Clustering Algorithm |
| title_full_unstemmed | Online Real-Time Trajectory Analysis Based on Adaptive Time Interval Clustering Algorithm |
| title_short | Online Real-Time Trajectory Analysis Based on Adaptive Time Interval Clustering Algorithm |
| title_sort | online real time trajectory analysis based on adaptive time interval clustering algorithm |
| topic | storm trajectory clustering adaptive data mining density grid |
| url | https://www.sciopen.com/article/10.26599/BDMA.2019.9020022 |
| work_keys_str_mv | AT jianjiangli onlinerealtimetrajectoryanalysisbasedonadaptivetimeintervalclusteringalgorithm AT huihuijiao onlinerealtimetrajectoryanalysisbasedonadaptivetimeintervalclusteringalgorithm AT jiewang onlinerealtimetrajectoryanalysisbasedonadaptivetimeintervalclusteringalgorithm AT zhiguoliu onlinerealtimetrajectoryanalysisbasedonadaptivetimeintervalclusteringalgorithm AT jiewu onlinerealtimetrajectoryanalysisbasedonadaptivetimeintervalclusteringalgorithm |