BigDataOcean Project: Early Anomaly Detection from Big Maritime Vessel Traffic Data
Konstantinos Chatzikokolakis, Dimitrios Zissis, Marios Vodas, Giannis Tsapelas, Spiros Mouzakitis, Panagiotis Kokkinakos, Dimitris Askounis,
COMPIT'19, 2019
Abstract
This paper discusses the concept and results of the BigDataOcean project, and specifically the anomaly detection pilot. While in the past, surveillance had suffered from a lack of data, current tracking technologies have transformed the problem into one of an overabundance of information, with needs which go well beyond the capabilities of traditional processing and algorithmic approaches. The major challenge faced today is developing the capacity to identify patterns emerging within huge amounts of data, fused from various sources and detecting outliers in a timely fashion, to act proactively and minimise the impact of possible threats. Within this context we first define an “anomaly”, beforeproceeding to present the BigDataOcean anomaly detection service; a service for the classification and early detection of anomalous vessel patterns. The service makes use of state-of-the-art big data technologies and novel algorithms which form the basis for a service capable of real time anomaly detection.
This work was deployed in the context of the EU funded BigDataOcean project.
.webp)
.webp)
Previous
Next
Navigating through dense waters: a toolbox for creating maritime density maps
A Big Data framework for Modelling and Simulating high-resolution hydrodynamic models in sea harbours
Modelling and simulating vessel emissions in real time based on terrestrial AIS data
Real-time maritime anomaly detection: detecting intentional AIS switch-off
A comparison of supervised learning schemes for the detection of search and rescue (SAR) vessel patterns
BigDataOcean Project: Early Anomaly Detection from Big Maritime Vessel Traffic Data
Countering Real-Time Stream Poisoning: An Architecture for Detecting Vessel Spoofing in Streams of AIS Data
Determining the bilge water waste risk and management in the Gulf of Antalya by the Monte Carlo method
FleetMon Supports the Development of Environmental Impact Assessment on the Brazilian Coast
Scrapping Probabilities and Committed CO2 Emissions of the International Ship Fleet
Scalable In-Database Machine Learning for the Prediction of Port-to-Port Routes
How a real-time-based sea traffic forecast helps to organize and optimize the flow of maritime goods
Generating a node in an AIS-based routing graph for improved Estimated Time of Arrival. (Big) Data challenge: using AIS for generating a routing graph
Composition, spatial distribution and sources of macro-marine litter on the Gulf of Alicante seafloor (Spanish Mediterranean)
Climate change, non-indigenous species and shipping: assessing the risk of species introduction to a high-Arctic archipelago
The Big Picture: An Improved Method for Mapping Shipping Activities
GMSA: A Digital Twin Application for Maritime Route and Event Forecasting
Patterns of Life : Global Inventory for maritime mobility patterns
Require AIS data for your project?
Please reach out to us to discuss which Kpler offering will support your research project or academic study in the best possible and efficient way.
