Data integrity vs. inference accuracy in large AIS datasets

TL;DR

This paper examines how data integrity affects inference accuracy in large AIS datasets and proposes methods to detect and correct errors, ultimately enhancing maritime safety and operational efficiency.

Contribution

It introduces error detection and correction techniques tailored for AIS data, demonstrating their effectiveness in improving inference accuracy.

Findings

Improved data integrity leads to higher classification accuracy.

Error correction methods significantly reduce data inaccuracies.

Enhanced data quality benefits maritime safety and traffic management.

Abstract

Automatic Ship Identification Systems (AIS) play a key role in monitoring maritime traffic, providing the data necessary for analysis and decision-making. The integrity of this data is fundamental to the correctness of infer-ence and decision-making in the context of maritime safety, traffic manage-ment and environmental protection. This paper analyzes the impact of data integrity in large AIS datasets, on classification accuracy. It also presents er-ror detection and correction methods and data verification techniques that can improve the reliability of AIS systems. The results show that improving the integrity of AIS data significantly improves the quality of inference, which has a direct impact on operational efficiency and safety at sea.