A Maritime Big Data Framework Integration in a Common Information Sharing Environment
Abstract
Ensuring a high level of vessel traffic surveillance and maritime safety is determined by exploiting innovative ICT technologies and international cooperation among maritime authorities. Therefore, initiatives for maritime surveillance, global and regional integrations are realised through a collaborative, cost-effective and interoperable Common Information Sharing Environment (CISE). Consisting of the institutional network of maritime authorities that cooperate on various domains like safety, border control, environmental and rescue missions at sea, CISE enables the efficient transfer and economic exchange of maritime data and information via different interoperable systems using modern digital technologies. The ever-increasing amount of data received from heterogeneous data sources requires specific processing through the adoption of a Big Data framework which hosts, manages and distributes data to maritime users, contributing with great overall benefits to the CISE network core functionality. Specifically, this paper analyses the advantages of the Data Lake infrastructure, including its processes, techniques, tools and applications used to enhance maritime surveillance and safety across the CISE network. This part contains the deployment and interoperability achieved through the components of the participating command and control (C2) systems. Last, as a case study, an overview of the EU project EFFECTOR is provided which aims to demonstrate an end-to-end interoperability framework of data-driven solutions for maritime situational awareness at strategic and tactical operations.
... For dataset location, discovery mechanisms are used. Those mechanisms employ graph or semantic databases that are used to implement metadata management and governance systems, often associated with the use of data lakes ( [10], [11], [12]). To simplify the queries, the solution proposed by the EFFECTOR Project uses a semantic layer which will also be described in section 3.2. ...
... In the end, a complete data lake system contains all the technological solutions that allow data to be stored in different formats, from the raw origin format to the more structured one, in order to serve as a reference point for the data of an entire system. The EFFECTOR solution embraces this concept and in addition to the raw data storage capacity includes additional modules for storage, these modules implement complementary features: the operation databases (DB) and the data warehouse ( [12], [13]). ...
Establishing an efficient information-sharing network among national agencies in the maritime domain is of essential importance in enhancing operational performance, increasing situational awareness, and enabling interoperability among all involved maritime surveillance assets. Based on various data-driven technologies and sources, the EU initiative of Common Information Sharing Environment (CISE), enables the networked participants to timely exchange information concerning vessel traffic, joint SAR & operational missions, emergency situations, and other events at sea. In order to host and process vast amounts of vessels and related maritime data consumed from heterogeneous sources (e.g. SAT-AIS, UAV, radar, METOC), the deployment of big data repositories in the form of Data Lakes is of great added value. The different layers in the Data Lakes with capabilities for aggregating, fusing, routing, and harmonizing data are assisted by decision support tools with combined reasoning modules with semantics aiming at providing a more accurate Common Operational Picture (COP) among maritime agencies. Based on these technologies, the aim of this paper is to present an end-to-end interoperability framework for maritime situational awareness in strategic and tactical operations at sea, developed in EFFECTOR EU-funded project, focusing on the multilayered Data Lake capabilities. Specifically, a case study presents the important sources and processing blocks, such as the SAT-AIS, CMEMS, and UAV components, enabling maritime information exchange in CISE format and communication patterns. Finally, the technical solution is validated in the project's recently implemented maritime operational trials and the respective results are documented.
... Big Data analytics can be used for vessel tracking and monitoring in real-time. Various existing sensors, such as AIS (Automatic Identification System), radar, LRIT (Long Range Identification and Tracking), and satellite imagery, collect data on vessels' positions, speeds, routes, and other relevant information [4]. This data can be used to improve maritime safety and security, route planning, fuel consumption efficiency and ensure vessels are on schedule ( [2], [3], [5]). ...
This paper reviews the most important cases of using Blockchain to support Big Data in maritime transport and supply chains and to make them secure and integrated. Contemporary global markets and trade produce a vast amount of Big Data that is collected from various sources and processed, structured, and categorized in order to provide important information to various users in the maritime sector. Also, Blockchain as a new disruptive technology could provide important benefits for handling, securing, and efficient management of Big Data within the maritime transportation supply chain. The paper presents some of the key platforms of Blockchain for maritime and logistics purposes, including smart contracts and other use cases.
... An example closer to the military domain is [24], where a multi-layered data lake is the basis for sharing heterogeneous maritime surveillance data in order to provide better decision support for maritime safety and security agencies in the EU. This data lake is layered into tactical and strategic levels in order to ensure that command structures have proper access to data in their area of responsibility and that data can be propagated to the right level when requested. ...
Providing relevant sensor data for military decisionmakers to build and maintain their situational awareness is apersistent problem, preventing proper utilization of collectedsensor data. In this paper, we propose to mitigate this by usinga data lake solution controlled by a data catalog modelling itsmetadata according to SpatioTemporal Asset Catalog (STAC), aspecification from the satellite imagery community. The feasibilityof this setup is illustrated by a military maritime use case backedby an experimental sensor data infrastructure.
... The Data Ingestion and Harmonization Service is the entry point of data into the VesselAI platform. It ingests both batch and streaming data sources using different techniques suited to each data type (Paladin et al., 2022). Batch data sources can consist of local files, web resources or legacy databases, while streaming data sources consist of web streams and message brokers. ...
The modern maritime industry is producing data at an unprecedented rate. The capturing and processing of such data is integral to create added value for maritime companies and other maritime stakeholders, but their true potential can only be unlocked by innovative technologies such as extreme-scale analytics, AI, and digital twins, given that existing systems and traditional approaches are unable to effectively collect, store, and process big data. Such innovative systems are not only projected to effectively deal with maritime big data but to also create various tools that can assist maritime companies, in an evolving and complex environment that requires maritime vessels to increase their overall safety and performance and reduce their consumption and emissions. An integral challenge for developing these next-generation maritime applications lies in effectively combining and incorporating the aforementioned innovative technologies in an integrated system. Under this context, the current paper presents the architecture of VesselAI, an EU-funded project that aims to develop, validate, and demonstrate a novel holistic framework based on a combination of the state-of-the-art HPC, Big Data and AI technologies, capable of performing extreme-scale and distributed analytics for fuelling the next-generation digital twins in maritime applications and beyond.
... MARITIME BIG DATA FRAMEWORK EXAMPLES Maritime Big Data are supported by software tools, known as Big Data Analytics, with the ability to fast process and extract the queried vessel or other maritime information from large databases, visualize it through Business Intelligence (BI) software and help make a proper decision for maritime authorities. On top of this approach and including the CISE Network, a similar architecture in the form of Maritime Big Data Framework could be established by integration of data collected from national sources, transferred over Inter-VTS Exchange Format (IVEF) or National Marine Electronics Association (NMEA) protocols to databases in multifunctional DL with layers for ingestion, aggregation, semantics, data fusion and analytics, and harmonization [10]. ...
In this paper, we present the core applications of data lakes and other big data infrastructure technologies for the purpose of enhancing the maritime interoperability framework and ensuring resilient collaboration among agencies. The approach is based on the deployment of multi-layered & semantically enabled Data Lakes for storing various maritime data collected from heterogeneous sensors, and on the information exchange process through the Common Information Sharing Environment (CISE) network using advanced Command and Control (C2) platforms. The results of this paper are derived from the EU-funded project EFFECTOR, highlighting the significant contribution of advanced solutions using Artificial Intelligence algorithms and supporting UAV and C2 technologies to various operations at sea. The validation survey results collected from end-users after the execution of the maritime trials are presented as well.
This paper outlines an extensive analysis of the case of Montenegro’s maritime surveillance system becoming integrated within the European Common Information Sharing Environment (CISE). Threats to secure maritime borders across Europe are ever-present and regularly demand coordinated efforts between the member states to tackle and prevent them, e.g. illegal immigration across the Mediterranean. Administration for Maritime Safety and Port Management (AMSPM) in Montenegro is a member of the ANDROMEDA EU project that seeks to facilitate deployments and demonstrations of CISE trials across the European regions, towards their endorsement readiness. AMSPM is now at the forefront of assessing and deploying the CISE components in Montenegro. It thus appropriately evaluates the operational aspects, observes the CISE implementations in some European states, formulates the impact for other national stakeholders, as well as the very prospect of the resulting augmented maritime surveillance in the country. This substantiates the content of this paper as the feasibility of the CISE deployment in Montenegro, supported by a snapshot of the cost-benefit analysis. We aspire to offer novel perspectives and insights that could be a universally useful experience to different CISE implementation initiatives, especially for countries or regions of similar smaller sizes and coastal area.
Implementations of the rising EU maritime initiative, namely the Common Information Sharing Environment (CISE), involves network connectivity and data sharing processes among EU Member States agencies. These interactions occur at the national, regional and international levels with the principal purpose to increase maritime borders safety, security and effectiveness. The developed infrastructure of the CISE application augments the use of maritime Command and Control (C2) functions, enabling an enhanced Common Operational Picture (COP), monitoring, interoperability, improved situational awareness, and safety/security missions. We outline a case
of regional interconnections of maritime surveillance systems and data sources integrated via CISE network within collaborations of Maritime Authorities, border control agencies, IT industry and researchers participating in the international EU ANDROMEDA H2020 project. This paper presents the operations of the Administration for
Maritime Safety and Port Management of Montenegro (AMSPM), partner and an end-user in the ANDROMEDA project, during C2 systems’ exploitation in the maritime safety domain. Specifically, the regional Adriatic-Ionian integration of maritime authorities’ legacy systems for monitoring and surveillance, with the application of highlevel operational C2 systems, fully compliant to the enhanced maritime CISE data model, is proposed in order to valorise regional potentials from strategic and safety aspects. We provide some experiences/results of maritime C2 operations and use cases in AMSPM during the Adriatic-Ionian trial period of the ANDROMEDA project, showing the potential benefits of integrating Montenegro, as
an EU candidate country, in the regional CISE network. Thus, EU agencies have interests in the proposed CISE extension, since Montenegro provides great potentials for information exchange contributions to the EU CISE network’s full operability.
In this paper, the authors perform a literature review of the drivers, success factors and barriers to digital transformation in the maritime transport sector. Previous research offering a comprehensive overview of digital transformation in the maritime transport sector is scarce. In order to fill this research gap, the authors have identified a total of 139 sources, mainly related to the drivers, success factors and barriers for digitalization and digital transformation. The analysis of the state of the art was performed, along with the analysis of the impact of digital transformation in the maritime transport sector using a number of cases. The development of innovative technologies (such as Blockchain or autonomous shipping) definitely fosters digital transformation in the maritime transport sector. The barriers which are slowing down digital transformation compared to other industries are highlighted, such as the lack of awareness of how digital transformation may affect the business, and the lack of standards and cooperation among stakeholders. The research findings fill the identified research gap, and can serve practitioners in shaping up proper strategies for successful digital transformation of organizations in the maritime transport sector.
The Common Information Sharing Environment (CISE) is an ongoing cooperative development initiative that incrementally incorporates new participants and countries through integrations of facilities for European maritime surveillance. This paper outlines some key processes and technicalities required for becoming a part of the CISE, specifically for the case of a maritime surveillance department in Montenegro-Administration for Maritime Safety and Port Management (AMSPM). The content greatly derives from an ongoing European Union (EU) collaborative project-ANDROMEDA and shows the CISE components such as its data model, services and architecture compositions fitting with existing legacy systems for maritime surveillance. We also show some extracts from the Adriatic-Ionian trial, being conducted in the project, using the enhanced CISE features and involving partners from Italy, Greece and Montenegro. The examinations and general guidelines presented are particularly intended to give insights into operational capabilities for search and rescue missions, oil spills responses and general sea occurrences and conditions monitoring.
The paper focuses on assessing the level of digitalization in several developing maritime business environments in Albania, Bosnia and Herzegovina, Montenegro, and Serbia. The assessment has been done in reference to Holtham's and Courtney's Intelligent Information and Communication Technologies (ICT) Exploiter Model. The dimensions as maritime business system effectiveness, roles, and skills of information technology personnel, ladders of knowledge, ICT strategy, organizational culture, and manager's mindset are analyzed. In addition, benchmarking with findings from developed maritime business environments in Croatia, Greece, Italy, and Slovenia, which belong to the European Union (EU), by using the same model, has been conducted. This is done with the aim to outline directions for improving the quality and speed of digitalization in non-EU countries, which have been functioning for decades in transitional conditions. The maritime ecosystem naturally has a tendency to be unique and to function smoothly as such. Alleviating the differences in the level and effectiveness of digitalization in developed and developing European countries is a path towards achieving this goal. By sharing their own expertise in the rational and intelligent use of ICT, developed EU countries can support developing non-EU countries towards ensuring sustainability in the entire European and worldwide maritime business ecosystem.
As maritime transport produces a large amount of data from various sources and in different formats, authors have analysed current applications of Big Data by researching global applications and experiences and by studying journal and conference articles. Big Data innovations in maritime transport (both cargo and passenger) are demonstrated, mainly in the fields of seaport operations, weather routing, monitoring/tracking and security. After the analysis, the authors have concluded that Big Data analyses can provide deep understanding of causalities and correlations in maritime transport, thus improving decision making. However, there exist major challenges of an efficient data collection and processing in maritime transport, such as technology challenges, challenges due to competitive conditions etc. Finally, the authors provide a future perspective of Big Data usage in maritime transport.
The definition and characteristics of big data are summarized. From the era of big data, the production of command and control system data leads to 3 issues of data storage and processing, situation generation and decision-making, and data visualization. It takes the construction of a big data center and discusses the basics. The accusation framework and the coping strategies adopted by cloud computing under big data conditions provide reference for the application of big data technology in command and control systems, and point out the development trend of the next instruction system.
The timely and efficient cooperation across sectors and borders during maritime crises is paramount for the safety of human lives. Maritime monitoring authorities are now realizing the grave importance of cross-sector and cross-border information sharing. However, this cooperation is compromised by the diversity of existing systems and the vast volumes of heterogeneous data generated and exchanged during maritime operations. In order to address these challenges, the EU has been driving several initiatives, including several EU-funded projects, for facilitating information exchange across sectors and borders. A key outcome from these efforts is the Common Information Sharing Environment (CISE), which constitutes a collaborative initiative for promoting automated information sharing between maritime monitoring authorities. However, the adoption of CISE is substantially limited by its existing serialization as an XML Schema only, which facilitates information sharing and exchange to some extent, but fails to deliver the fundamental additional benefits provided by ontologies, like the richer semantics, enhanced semantic interoperability and semantic reasoning capabilities. Thus, this paper presents EUCISE-OWL, an ontology representation of the CISE data model that capitalizes on the benefits provided by ontologies and aims to encourage the adoption of CISE. EUCISE-OWL is an outcome from close collaboration in an EU-funded project with domain experts with extensive experience in deploying CISE in practice. The paper also presents a representative example for handling information exchange during a maritime crisis as well as performance results for specific querying tasks that can demonstrate and evaluate the use of the proposed ontology in practice.
