Assessing compression algorithms to improve the efficiency of clustering analysis on AIS vessel trajectories. Issue 3 (4th March 2023)

• Record Type: Journal Article
• Authors: Ferreira, Martha Dais; Campbell, Jessica; Purney, Evan; Soares, Amilcar; Matwin, Stan
• Is Part Of: International journal of geographical information science. Volume 37:Issue 3(2023)
• Page Start: 660
• Page End: 683
• Publication Date: 2023-03-04
• Subjects: Anomaly detection -- trajectories compression -- AIS data -- time-series -- clustering; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2022.2163494
• Languages: English
• ISSNs: 1365-8816

Abstract

Abstract: In the maritime environment, the Automatic Identification System (AIS) is used to monitor vessel activity concerning security and safety ocean-wide. AIS data has been used to detect anomalous behaviors related to suspicious activities and hazardous events. Typically, clustering analysis is used to investigate anomalous events within the AIS data stream. However, the main challenge in this approach is to determine and execute the dissimilarity measure between trajectories since they differ in size and time. In addition, these calculations are computationally expensive and not scalable. To tackle this issue, compression algorithms can be applied to perform clustering analysis since they are typically used to reduce storage and processing time. Therefore, the proposed analysis will assess how compression algorithms affect clustering results with respect to detecting anomalous vessel trajectories. The analysis results show that a suitable compression algorithm can reduce the overall processing time with little impact on the clustering results while supporting the scalability of this type of analysis.