# Composite Event Recognition for Maritime Monitoring

arXiv: https://arxiv.org/abs/1903.03078 · HTML: https://mlsanity.com/paper/1903.03078/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2019.

## Built on by (most cited first, 4 of 4)

- [Representation and Processing of Instantaneous and Durative Temporal Phenomena](https://mlsanity.com/paper/2108.13365/) (2021)
- [Early Time-Series Classification Algorithms: An Empirical Comparison](https://mlsanity.com/paper/2203.01628/) (2022)
- [XLOG: A CUDA-Native Engine for Neurosymbolic Integration](https://mlsanity.com/paper/2609.27203/) (2026)
- [LLMs for Executable Multi-Agent System Specification Generation](https://mlsanity.com/paper/2609.34619/) (2026)

## Abstract (arXiv)

Maritime monitoring systems support safe shipping as they allow for the real-time detection of dangerous, suspicious and illegal vessel activities. We present such a system using the Run-Time Event Calculus, a composite event recognition system with formal, declarative semantics. For effective recognition, we developed a library of maritime patterns in close collaboration with domain experts. We present a thorough evaluation of the system and the patterns both in terms of predictive accuracy and computational efficiency, using real-world datasets of vessel position streams and contextual geographical information.