Enhancing Maritime Data Integration for Platform Services with Sequence-to-Sequence Models and Statistical Refinement

Abstract

The increasing adoption of IoT devices on ships and the expansion of platform services present a critical challenge in integrating heterogeneous ship data models into a unified platform data model. The task involves mapping ship Domain-Specific Language (DSL) descriptions to platform indices, complicated by variability and class imbalance. To address these challenges, this paper proposes a framework that combines a sequence-to-sequence model with statistical vectorization techniques. The model generates structured mapping classes, offering flexibility to accommodate diverse equipment and attributes, while training exclusively on connected data mitigates class imbalance. Subsequently, statistical vectorization techniques are applied to identify the correct match among the classified candidates, while ensuring that unconnected data is excluded. This two-step approach enhances recall and guarantees accurate relationships between ship DSLs and platform data indices. The proposed framework is validated using real-world data from 52 ships. Experimental results demonstrate that the sequence-to-sequence model with statistical refinement outperformed single-step and discriminative methods in handling class imbalance and variations when mapping ship DSLs to a unified platform data model. Our method achieved a recall of 89.14 and an Fβ-Score of 87.12, which are 4.15 and 1.91 points higher, respectively, than the reference classification method.