Assessing compression algorithms to improve the efficiency of clustering analysis on AIS vessel trajectories-Reference-Cited by-同舟云学术

Assessing compression algorithms to improve the efficiency of clustering analysis on AIS vessel trajectories

Published:2023-02-14 Issue:3 Volume:37 Page:660-683
ISSN:1365-8816
Container-title:International Journal of Geographical Information Science
language:en
Short-container-title:International Journal of Geographical Information Science

Author:

Ferreira Martha Dais¹^ORCID,Campbell Jessica²,Purney Evan³,Soares Amilcar⁴,Matwin Stan¹⁵

Affiliation:

1. Faculty of Computer Science, Dalhousie University, Halifax, Canada

2. Maritime Systems, Experimentation and Analytics, Defence Research and Development Canada, Dartmouth, Canada

3. General Dynamics Mission Systems – Canada, Ottawa, Ontario, Canada

4. Department of Computer Science, Memorial University of Newfoundland, St. John's, Canada

5. Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland

Funder

Natural Sciences and Engineering Research Council of Canada

CHIST-ERA

NCN

Publisher

Informa UK Limited

Subject

Library and Information Sciences,Geography, Planning and Development,Information Systems

Link

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2022.2163494

Reference46 articles.

1. Abreu, F.H.O. et al., 2021a. Local anomaly detection in maritime traffic using visual analytics. In: EDBT/ICDT Workshops: 4th International Workshop on Big Mobility Data Analytics (BMDA 2021), Nicosia, Cyprus.

2. A Trajectory Scoring Tool for Local Anomaly Detection in Maritime Traffic Using Visual Analytics

3. Alam, M.M., and Torgo, L., 2022. A clustering-based approach for predicting the future location of a vessel. Proceedings of the 35th Canadian Conference on Artificial Intelligence. Toronto, Canada: Canadian Artificial Intelligence Association.

4. Andersen, A.S. et al., 2021. Ais data as trajectories and heat maps. Proceedings of the 29th International Conference on Advances in Geographic Information Systems. Beijing, China: Association for Computing Machinery (ACM), 431–434.

5. Density-Based Clustering Based on Hierarchical Density Estimates

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