Abstract
The shipping industry deals with an amount of information much of which is not properly stored or secured and ends up getting lost over time. However these data become crucial in times of incidents. In the future ships will require Big Data Analytics for purposes such as condition monitoring, auto-piloting, freight tracking and shipbuilding. The progress in Big Data will enable ships to communicate with each other through condition monitoring systems and engines. As a result Big Data Analytics enhances both the safety and efficiency of the industry. It is important to store the data from classification societies and shipbuilders, for references and advancements where Big Data Analytics plays a significant role. Temporal data mining is a field that focuses on analyzing ordered data streams with interdependencies. In this work, the goal is to detect anomalies in maritime vessel data, particularly sudden speed changes and unusual course deviations. This study presents an evaluation of the detection accuracy and identify the most effective algorithm for anomaly detection in the context of maritime activities.
