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