Operational shipping intelligence through distributed cloud computing

Dragos Sebastian Cristea, Liliana Moga, Mihaela Neculita, Olegas Prentkovskis, Khalil Md Nor and Abbas Mardani

Journal of Business Economics and Management, 2017, vol. 18, issue 4, 695-725

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.

Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.3846/16111699.2017.1329162 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jbemgt:v:18:y:2017:i:4:p:695-725

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TBEM20

DOI: 10.3846/16111699.2017.1329162

Access Statistics for this article

Journal of Business Economics and Management is currently edited by Izolda Joksiene, Romualdas Ginevicius and Ieva Meidute

More articles in Journal of Business Economics and Management from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().