Share:


Operational shipping intelligence through distributed cloud computing

    Dragos Sebastian Cristea Affiliation
    ; Liliana Mihaela Moga Affiliation
    ; Mihaela Neculita Affiliation
    ; Olegas Prentkovskis Affiliation
    ; Khalil Md Nor Affiliation
    ; Abbas Mardani Affiliation

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.

Keyword : transportation, business, cloud computing, competence, ship, maritime company, automatic ship performance analysis

How to Cite
Cristea, D. S., Moga, L. M., Neculita, M., Prentkovskis, O., Nor, K. M., & Mardani, A. (2017). Operational shipping intelligence through distributed cloud computing. Journal of Business Economics and Management, 18(4), 695-725. https://doi.org/10.3846/16111699.2017.1329162
Published in Issue
Aug 27, 2017
Abstract Views
437
PDF Downloads
610
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.