Critical maritime events present substantial social, environmental, and economic risks, particularly as climate change increases the frequency and severity of hazardous weather along major maritime trade routes. This paper presents a weather-aware vessel rerouting framework that integrates real-time meteorological forecasts with large-scale historical vessel traffic patterns from the Kpler MarineTraffic Platform observed under diverse sea conditions, aiming to enhance navigational safety during extreme weather events while maintaining maritime operational efficiency. The core of the proposed approach is a modified A* search algorithm, where edge weights are dynamically assigned based on forecasted weather severity and historical vessel trip density, as a function of the prevailing sea state. The approach is evaluated using randomly selected origin-destination pairs across the southeastern U.S. Coast in September 2022, a period when a range of weather scenarios in terms of severity, spatial extent, and duration. Results indicate a 16.97% reduction in median cumulative weather penalties in variable sea conditions and a 35.03% decrease very extreme sea conditions when compared to shortest-path routing. A case study for Hurricane Fiona (Category 4, 21 September 2022) further demonstrates the system's ability to entirely avoid areas forecasted to experience extreme sea conditions. Findings highlight the value of integrating AIS-based collective fleet intelligence with weather data from historical databases to improve voyage planning and vessel operations in dynamically changing sea conditions.

Monitoring vessel traffic on a global scale is a complex and challenging task. The large number of moving vessels and the complexity of monitoring their position and forecasting their route in real-time require novel, advanced and highly scalable big-data mechanisms. In this work a digital twin for constant maritime situational awareness on a global scale is presented. The described multi-layered system is able to visualize maritime traffic in real-time, based on data from the Automatic Identification System (AIS), while also providing forecasts of future movement based on machine learning and deep learning techniques. The system is validated using real streaming AIS data from around the globe to demonstrate its performance, scalability and parallelization efficiency.

We present a Maritime Situational Awareness (MSA) framework for detecting and forecasting maritime events (e.g., illegal fishing) over streams of Big maritime Data. The architecture of the MSA framework relies on the following state-of-the-art components: (i) the Maritime Event Detector which uses data-driven distributed techniques deployed on a computer cluster to detect maritime events of interest in an online, real-time fashion, (ii) the Complex Event Forecasting module, which implements state-of-the-art distributed Complex Event Forecasting techniques for maritime data, (iii) the Synopses Data Engine component, that creates synopses of maritime data improving the scalability of the framework and (iv) the streaming extension of a popular data science platform, namely RapidMiner Studio, that integrates all the above, allowing users to graphically design and rapidly implement Big Data analytics pipelines which can be deployed transparently on top of distributed architectures.

In this paper, we propose an efficient in-DBMS solution for the problem of sub-trajectory clustering and outlier detection in large moving object datasets. The method relies on a two-phase process: a voting-and-segmentation phase that segments trajectories according to a local density criterion and trajectory similarity criteria, followed by a sampling-and-clustering phase that selects the most representative sub-trajectories to be used as seeds for the clustering process. Our proposal, called S 2 T-Clustering (for Sampling-based Sub-Trajectory Clustering) is novel since it is the first, to our knowledge, that addresses the pure spatiotemporal sub-trajectory clustering and outlier detection problem in a real-world setting (by ‘pure’ we mean that the entire spatiotemporal information of trajectories is taken into consideration). Moreover, our proposal can be efficiently registered as a database query operator in the context of extensible DBMS (namely, PostgreSQL in our current implementation). The effectiveness and the efficiency of the proposed algorithm are experimentally validated over synthetic and real-world trajectory datasets, demonstrating that S 2 T-Clustering outperforms an off-the-shelf in-DBMS solution using PostGIS by several orders of magnitude.

Maritime monitoring is a typical Big Data problem where hundreds of thousands of vessels across the globe transmit messages about their location, speed and other information. We have developed a system for online vessel tracking that performs, as a first step, a high-rate but accurate trajectory compression. Subsequently, the compressed trajectories are analyzed by a complex event recognition engine, promptly reporting alerts to maritime authorities. To deal with realistic maritime event patterns, we seamlessly integrated spatial and temporal reasoning for online event recognition. The system is evaluated on real data from the Greek seas.

We present a system that combines intelligent online tracking with complex event recognition against streaming positions relayed from numerous vessels. Given the vital importance of maritime safety to the environment, the economy, and in national security, our sys-tem leverages the real-time acquisition of vessel activity with ge-ographical and other static information. Thus, it can offer timely notification in emergency situations, such as intrusion into marine preservation areas, loitering, and unsafe sailing. Thanks to a mobil-ity tracking module, evolving trajectories generated by massive po-sitional updates can be compressed online into concise, but reliable synopses per ship, retaining only salient motion features within a sliding window. These features are exploited by a complex event recognition module that detects suspicious situations of interest to maritime authorities. We conducted a comprehensive empirical validation against a real dataset of traces collected from thousands of vessels. Our results confirm the scalability and approximation accuracy of the proposed system, and thus demonstrate its poten-tial for effective, real-time maritime monitoring. 1.

Mobility data sources feed larger and larger trajectory databases nowadays. Due to the need of extracting useful knowledge patterns that improve services based on users' and customers' behavior, querying and mining such databases has gained significant attention in recent years. However, publishing mobility data may lead to severe privacy violations. In this paper, we present Private-HERMES, an integrated platform for applying data mining and privacy-preserving querying over mobility data. The presented platform provides a two-dimension benchmark framework that includes: (i) a query engine that provides privacy-aware data management functionality of the in-house data via a set of auditing mechanisms that protect the sensitive information against several types of attacks, and (ii) a progressive analysis framework, which, apart from anonymization methods for data publishing, includes various well-known mobility data mining techniques to evaluate the effect of anonymization in the querying and mining results. The demonstration of Private-HERMES via a real-world case study, illustrates the flexibility and usefulness of the platform for supporting privacy-aware data analysis, as well as for providing an extensible blueprint benchmark architecture for privacy-preservation related methods in mobility data.

Existing approaches for privacy-aware mobility data sharing aim at publishing an anonymized version of the mobility dataset, operating under the assumption that most of the information in the original dataset can be disclosed without causing any privacy violations. In this paper, we assume that the majority of the information that exists in the mobility dataset must remain private and the data has to stay in-house to the hosting organization. To facilitate privacy-aware sharing of the mobility data we develop a trajectory query engine that allows subscribed users to gain restricted access to the database to accomplish various analysis tasks. The proposed engine (i) audits queries for trajectory data to block potential attacks to user privacy, (ii) supports range, distance, and k-nearest neighbors spatial and spatiotemporal queries, and (iii) preserves user anonymity in answers to queries by (a) augmenting the real trajectories with a set of carefully crafted, realistic fake trajectories, and (b) ensuring that no user-specific sensitive locations are reported as part of the returned trajectories.