Operational Shipping Intelligence Through Distributed Cloud Computing

Abstract

This paper provides a conceptual architecture for a cloud based platform design, that implements continuously data storage and analysis services for large maritime ships, with the purpose to provide valuable insights for maritime transportation business. We do this by first identifying the need on the shipping market for such kind of systems and also the significance and impact of different factors related to shipping business processes. The architecture presented throughout this paper will be defined around some of the most currently used ICT technologies, like Amazon Cloud Services, Sql Server Databases, .NET Platform, Matlab 2016 or javascript visualization libraries. The proposed system makes possible for a maritime company to gain more knowledge for optimizing the efficiency of its operations, to increase its financial benefits and its competitive advantage. The platform architecture was designed to make possible the storage and manipulation of very large datasets, also allowing the possibility of using different data mining techniques for inferring knowledge or to validate already existent models. Ultimately, the developed methodology and the presented outcomes demonstrate a vast potential of creating better technological management systems for the shipping industry, starting from the challenges but also from the huge opportunities this sector can offer.

Intelligent Recognition System Based on Contour Accentuation for Navigation Marks

“…Recently, there are some intelligent information systems were reported about navigation mark management and service [32][33][34]. However, these systems were mainly developed based on telemetry and remote control; their identification mechanism of navigation marks is different from image recognition.…”

The disruptive impact of future advanced ICTs on maritime transport: a systematic review

“…), the congestion, capacity problems and delays in Europe’s freight transport system and the increasing dynamic and structural complexity of logistics systems are mentioned by Costa et al (2016). Other factors are the increased competition in the shipping business environment and the decreasing profit margins (Cristea et al , 2017). Numerous other factors are enumerated by Harris et al (2015), respectively: slow adoption of future advanced ICTs in multimodal freight transport in Europe; lack of effective and efficient information connectivity among and between various modes (water, air, road and rail) in multimodal transport; containerization as an enabler of multimodal transport; challenges for the multimodal transport because of the deployment of different ICTs by the various players (such as freight forwarders, third-party logistics service providers, couriers, carriers of different modes of transport, multimodal transport operators, rail, sea carriers, port and intermodal terminal operators); user-related, technology-related and policy-related barriers to the adoption of future advanced ICTs.…”

“…Examples of intervention that underline the development/design related to the future advanced ICTs in maritime transport are: elaboration of the ship traffic fundamental diagram that represents the speed-density relationship (Kang et al , 2018); development of an improved MAC (media access control) protocol design in the data exchange system for the Internet of Vessels (Zhang et al , 2018); development of the concept of an autonomous unmanned dry bulk carrier that is at least as safe as a manned vessel (Burmeister et al , 2014); creation of a decision support system for making decisions on vessel speed in maritime logistics (Lee et al , 2018); development of navigation strategies based on monitoring of fuel consumption, various emissions, use of lighting, heating (Mirović et al , 2018); development of a big data acquisition and analysis platform for intermodal transport (Xu et al , 2016); development of a conceptual architecture for a cloud-based platform design that implements continuously data storage and analysis services for large maritime ships (Cristea et al , 2017); and development of a solution to assess and communicate the uncertainties of the safety control (Wróbel et al , 2018b).…”

“…The drivers relative to information are: information sharing on logistic flows for traceability, collaboration and interoperability between different actors within the supply chain (Gnimpieba et al , 2015); need to address the issue of reduced visibility and limited information exchange (on real-time location and status of a container and its contents) between all stakeholders in the process of container consolidation within a port terminal (Tsertou et al , 2016); identification of the causes of cargo loss severity (Wu et al , 2017); need for clear and well tested examples on how modern technologies in storage and computations can be applied in real case scenarios (Cristea et al , 2017); and diminishing the information gap consisting in the absence of an objective study on the effect of unmanned ships on safety of maritime transport (Wróbel et al , 2017).…”

A Distributed Snapshot Protocol for Efficient Artificial Intelligence Computation in Cloud Computing Environments

“…In fact, underneath the artificial intelligence, a large amount of computation is performed [1,2]. Meanwhile, recent advances in cloud computing and services have led to a proliferation of supporting artificial intelligence applications [3,4]. The harmony of artificial intelligence and cloud computing provides a great opportunity in that data servers and computing servers in cloud computing environments hold the data for artificial intelligence applications and process them to make decisions, respectively [5][6][7].…”