Several modern day problems need to deal with large amounts of spatio-temporal data. As such, in order to meet the application requirements, more and more systems are adapting to the specificities of those data. The most prominent case is perhaps the data storage systems, that have developed a large number of functionalities to efficiently support spatio-temporal data operations. This work is motivated by the question of which of those data storage systems is better suited to address the needs of industrial applications. In particular, the work conducted, set to identify the most efficient data store system in terms of response times, comparing two of the most representative of the two categories (NoSQL and relational), i.e. MongoDB and PostgreSQL. The evaluation is based upon real, business scenarios and their subsequent queries as well as their underlying infrastructures and concludes in confirming the superiority of PostgreSQL in almost all cases with the exception of the polygon intersection queries. Furthermore, the average response time is radically reduced with the use of indexes, especially in the case of MongoDB.

In this work we propose a novel spatial knowledge discovery pipeline capable of automatically unravelling the “roads of the sea” and maritime traffic patterns by analysing voluminous vessel tracking data, as collected through the Automatic Identification System (AIS). We present a computationally efficient and highly accurate solution, based on a MapReduce approach and unsupervised learning methods, capable of identifying the spatiotemporal dynamics of ship routes and most crucially their characteristics, thus deriving maritime “patterns of life” at a global scale, without the reliance on any additional information sources or a priori expert knowledge. Experimental results confirm high accuracy of results and superior performance in comparison to other methods, with the entire processing duration completing in less than 3 hours for more than a terabyte of non-uniform spatial data. Finally, to clearly demonstrate the applicability and impact of our proposed method, we evaluate its ability to detect real world “anomalies”, such as maritime incidents reported in the European Marine Casualty Information Platform. Numerical results show the advantages of our scheme in terms of accuracy, with an achieved anomaly detection accuracy of higher than 93%, by detecting 313 out of 335 relevant maritime incidents.

This paper presents a recently developed Operational Forecast Platform (OFP) for prevailing sea conditions at very important ports worldwide (Accu-Waves). The OFP produces reliable high-resolution predictions of wave characteristics in and around ocean ports. Its goal is to support safer navigation, predict possible port downtime, assist vessel approaching, enhance management of towing services, and bolster secure ship maneuvering in busy ports around the globe. Accu-Waves OFP is based on integrated, high-resolution wave modelling over the continental shelf and in coastal areas that incorporates data from global- and regional-scale, open-sea wave and ocean circulation forecasts as boundary conditions. The coupling, nesting, calibration, and implementation of the models are reported and discussed in this paper, concerning 50 selected areas near and inside significant port basins. The detailed setup of the Accu-Waves OFP and its sub-system services is also provided regarding three-day forecasts at three-hourly intervals. The validation of the wave forecast system against in situ observations from wave buoys in coastal areas of the USA, Belgium, and Spain, as well as other model predictions by established OFPs, seems very promising, with performance skill scores ranging from adequate to very good. An exceptional case of stormy seas under severe marine weather conditions with very high wave maxima (>10 m) in the port of Algeciras is further discussed, confirming the good performance of the Accu-Waves OFP.

Density maps support a bird’s eye view of vessel traffic, through providing an overview of vessel behavior, either at a regional or global scale in a given timeframe. However, any inaccuracies in the underlying data, due to sensor noise or other factors, evidently lead to erroneous interpretations and misleading visualizations. In this work, we propose a novel algorithmic framework for generating highly accurate density maps of shipping activities, from incomplete data collected by the Automatic Identification System (AIS). The complete framework involves a number of computational steps for (1) cleaning and filtering AIS data, (2) improving the quality of the input dataset (through trajectory reconstruction and satellite image analysis) and (3) computing and visualizing the subsequent vessel traffic as density maps. The framework describes an end-to-end implementation pipeline for a real world system, capable of addressing several of the underlying issues of AIS datasets. Real-world data are used to demonstrate the effectiveness of our framework. These experiments show that our trajectory reconstruction method results in significant improvements up to 15% and 26% for temporal gaps of 3–6 and 6–24 h, respectively, in comparison to the baseline methodology. Additionally, a use case in European waters highlights our capability of detecting “dark vessels”, i.e., vessel positions not present in the AIS data.