L. Shen and P. R. Stopher, Review of GPS travel survey and GPS data-processing methods, Transport Reviews, vol. 34, no. 3, pp. 316-334, 2014.
G. Cugola and A. Margara, Processing flows of information: From data stream to complex event processing, ACM Computing Surveys, vol. 44, no. 3, pp. 1-62, 2012.
F. Zhu, P. Chen, D. Yang, W. Zhang, H. Chen, and B. Zang, A GPU-based high-throughput image retrieval algorithm, in Proceedings of the 5th Annual Workshop on General Purpose Processing with Graphics Processing Units, New York, NY, USA, 2012, pp. 30-37.
Z. Fang, D. Yang, W. Zhang, H. Chen, and B. Zang, A comprehensive analysis and parallelization of an image retrieval algorithm, in IEEE International Symposium on Performance Analysis of Systems and Software, Austin, TX, USA, 2011, pp. 154-164.
W. Zhang, T. Bao, B. Zang, and C. Zhu, Optimizing bandwidth constraint through register interconnection for stream processors, in Proceedings of the 16th International Conference on Parallel Architecture and Compilation Techniques, Brasov, Romania, 2007, pp. 199-208.
R. Evans, Apache storm, a hands on tutorial, in Proc. of IEEE International Conference on Cloud Engineering, Tempe, AZ, USA, 2015, p. 2.
P. Nesi, G. Pantaleo, and G. Sanesi, A hadoop-based platform for natural language processing of web pages and documents, Journal of Visual Languages & Computing, vol. 31, pp. 130-138, 2015.
S. S. Situ, Design and implementation of real-time traffic information processing system based on storm, (in Chinese), master degree thesis, Zhongshan University, Guangzhou, China, 2015.
S. S. Li, Design and implementation of real-time traffic information management system based on storm, (in Chinese), master degree thesis, Yangzhou University, Yangzhou, China, 2017.
F. Mazzarella, M. Vespe, D. Damalas, and G. Osio, Discovering vessel activities at sea using AIS data: Mapping of fishing footprints, in Proc. of International Conference on Information Fusion, Salamanca, Spain, 2014, pp. 1-7.
F. Mazzarella, V. F. Arguedas, and M. Vespe, Knowledge-based vessel position prediction using historical AIS data, in Proc. of Sensor Data Fusion: Trends, Solutions, Applications, Bonn, Germany, 2015, pp. 1-6.
S. Kim, H. Kim, and Y. Park, Early detection of vessel delays using combined historical and real-time information, Journal of the Operational Research Society, vol. 68, no. 2, pp. 1-10, 2016.
B. Ristic, B. L. Scala, M. Morelande, and N. Gordon, Statistical analysis of motion patterns in AIS data: Anomaly detection and motion prediction, in Proc. of International Conference on Information Fusion, Cologne, Germany, 2008, pp. 1-7.
R. Laxhammar, G. Falkman, and E. Sviestins, Anomaly detection in sea traffic - A comparison of the Gaussian mixture model and the kernel density estimator, in Proc. of International Conference on Information Fusion, Seattle, WA, USA, 2009, pp. 756-763.
S. Gaffney and P. Smyth, Trajectory clustering with mixtures of regression models, in Proc. of ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, San Diego, CA, USA, 1999, pp. 63-72.
S. N. Shang, Design and implementation of massive AIS message data mining system based on cloud computing and distributed technology, (in Chinese), master degree thesis, Dalian Maritime University, Dalian, China, 2017.
H. S. Qiu, Research on forecasting ship sailed track behavioral abnormalities algorithm based on Kalman filter, (in Chinese), master degree thesis, Hebei University of Technology, Tianjin, China, 2012.
A. Toshniwal, S. Taneja, A. Shukla, K. Ramasamy, J. M. Patel, S. Kulkarni, J. Jackson, K. Gade, M. Fu, J. Donham, et al. Storm@twitter, in Proc. of ACM SIGMOD International Conference on Management of Data, Snowbird, UT, USA, 2014, pp. 147-156.
D. Simonassi, G. Eisbruch, and J. Leibiusky, Getting Started with Storm. Sebastopol, CA, USA: O’Reilly Media Inc., 2012.
D. Vohra, Apache flume, in Proc. of Practical Hadoop Ecosystem, Berkeley, CA, USA, 2016, pp. 287-300.
K. Thein, Apache Kafka: Next generation distributed messaging system, Journal of Scientific Engineering and Technology Research, vol. 3, no. 47, pp. 9478-9483, 2014.
L. O’Callaghan, N. Mishra, A. Meyerson, S. Guha, and R. Motwani, Streaming-data algorithms for high-quality clustering, in Proc. of International Conference on Data Engineering, San Jose, CA, USA, 2002, p. 685.
C. C. Aggarwal, J. Han, J. Wang, T. J. Watson, and P. S. Yu, A framework for clustering evolving data streams, in Proceedings of the 29th International Conference on Very Large Data Bases, VLDB Endowment, Berlin, Germany, 2003, pp. 81-92.
C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu, A framework for projected clustering of high dimensional data streams, in Proc. of Thirtieth International Conference on Very Large Data Bases, VLDB Endowment, Toronto, Canada, 2004, pp. 852-863.
W. R. Jia, Research on clustering analysis algorithm for real time data stream, (in Chinese), master degree thesis, North China Electric Power University, Beijing, China, 2017.
S. W. Li, Research of data stream clustering methods based on density, (in Chinese), master degree thesis, Xidian University, Xi’an, China, 2017.
X. Cai, Application and development of AIS, Mechanical and Electrical Equipment, vol. 28, no. 2, pp. 28-30, 2011.