WCSE 2019 SUMMER ISBN: 当前:978-981-14-1684-2
DOI: 10.18178/wcse.2019.06.102

A Hadoop-based Co-occurrence Pattern Mining Model on AIS data

Bao Lei

Abstract— AIS is a tracking and self-reporting system used by maritime vessels to exchange information with other ships, AIS base stations, and satellites.Co-occurrence mining in AIS data can measure the proximity of ships in space and time and can be used in maritime traffic monitoring or other security purpose.Most of the current existing approaches can not meet the practical needs of large-scale ship trajectory data mining due to the lack of designing on parallel computing architecture and are insensitive to the spatial data characteristics.A model on co-occurrence pattern mining based on Hadoop is presented in this paper.By using parallel partitioning on the original data set, the mining in ship trajectory data is implemented on an extended MapReduce architecture.The experiments on real AIS data sets show that the large-scale ship trajectory data can be processed effectively , and the efficiency and correctness are maintained.

Index Terms&mdash, Spatiotemporal Data Mining, Spatiotemporal Co-occurrence, Apriori, AIS

Bao Lei
Computer Science Department, Wuhan Donghu University, CHINA


Cite: Bao Lei, "A Hadoop-based Co-occurrence Pattern Mining Model on AIS data," Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering, pp. 689-696 Hong Kong, 15-17 June, 2019.