In the last 25 years, information systems have had a disruptive effect on society and business. Up until recently though, the majority of passengers and goods were transported by sea in many ways similar to the way they were at the turn of the previous century. Gradually, advanced information technologies are being introduced, in an attempt to make shipping safer, greener, more efficient and transparent. The emerging field of Maritime Informatics studies the application of Information Technology and Information Systems to maritime transportation. Maritime informatics can be considered as both a field of study and domain of application. As an application domain, it is the outlet of innovations originating from Data Science and Artificial Intelligence, while as a field of study, it sits on the fence between Computer Science and Marine Engineering. Its complexity lies within this duality, as it is faced with disciplinary barriers while demanding a systemic transdisciplinary approach.

At present, there is a growing body of knowledge developing, which remains undocumented in a single source or textbook designed to ease students and practitioners into this new field. The objective of this book is to collect the material required for an undergraduate or postgraduate student to develop the core knowledge of this domain, in an analytical approach through real-world examples and case studies. The aim is to present our audience with an overview of the main technological innovations which are having a disruptive effect on the maritime industry, describe their principal ideas, methods of operation and applications, and discuss future developments. The book is designed in such a way as to first introduce required knowledge, algorithmic approaches and technical details, before presenting real-world applications.

The book is structured around four main pillars. First, we focus on Maritime Data. Bereta et al. present a comparative analysis of the so-called Maritime Reporting Systems, such as the well-known Automatic Identification System (AIS). Moreover, Tzouramanis draws up a compilation of reliable, freely available maritime datasets. This way, readers are set on the path to successfully finding and selecting the data that fit their needs, and to navigating effortlessly the sea of on-line information.

Second, we present key techniques for Off-line Maritime Data Processing. Etienne et al. provide a step-by-step guide for building a relational maritime database, thus supporting the investigation of maritime traffic and vessel behaviour. Tampakis et al. present pre-processing techniques for cleaning, transforming and partitioning long GPS traces into meaningful portions of vessel movement, and overview the representative maritime knowledge discovery techniques, such as trajectory clustering, group behaviour identification, hot-spot analysis, frequent route and network discovery, and data-driven predictive analytics. Subsequently, Andrienko and Andrienko show how Visual Analytics approaches can help in exploring properties of the maritime data, detecting problems and finding ways to clean and improve the data.

Third, we present three key steps for On-line Maritime Data Processing. Patroumpas presents techniques for maintaining summarised representations of vessel trajectories in an on-line fashion, based on AIS data streams. Santipantakis et al. present algorithms for on-line link discovery, i.e. identifying spatio-temporal relations between vessels, and areas of interest, such as when two vessels are close to each other, or when a vessel sails within a protected area. Then, Pitsikalis and Artikis present a formal computational model for combing the compressed trajectories and the spatio-temporal relations, in order to detect, in real-time, composite maritime activities, such as ship-to-ship transfer and (illegal) fishing.

Fourth, we focus on Applications. Jousselme et al. present a comparative analysis of uncertainty representation and reasoning techniques with respect to maritime route deviation. Ducruet et al. apply graph theory and complex network methods to AIS data, in order to analyse the key properties of maritime networks. Finally, Adland shows how AIS data may guide data-driven market analysis in shipping. He outlines how AIS data may be used to track commodity flows and analyse the key variables for fleet efficiency, supply and demand. To facilitate understanding, and allow for a consistent presentation, all chapters illustrate the presented techniques using AIS data. In particular, Chapters 3-9 use the open, heterogeneous, integrated dataset for maritime intelligence that concerns the area of Brest, France.

Moreover, the book is accompanied by a dedicated web page with on-line educational material, including datasets, SQL queries, algorithm implementations and visualisations:

http://cer.iit.demokritos.gr/maritime-informatics

The web page will be constantly updated, offering an up-to-date source of information for students and instructors. We hope that you will enjoy reading this book and that you will find answers to many related questions within. The overall aim has been to provide the audience with an informative book which will help introduce students to a range of related topics, while supporting practitioners in staying up to date with this fast-changing field.

Acknowledgments

We are indebted to the authors of all chapters for making this book possible. We would also like to thank Manolis Pitsikalis and Alexandros Troupiotis-Kapeliaris for their invaluable, constant support in producing the book and its on-line material.

We have been supported by the INFORE project, which received funding from the European Union’s Horizon 2020 research and innovation programme, under grant agreement No 825070.