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INVESTIGATING THE POTENTIAL OF DATA ANALYTICS SOLUTIONS IN MARITIME INDUSTRY

Year 2017, Volume: 5 Issue: 1, 67 - 70, 30.06.2017
https://doi.org/10.17261/Pressacademia.2017.573

Abstract

Recently,
enhancing the data collection and utilization in maritime field is one of the
key issue. At this stage, the maritime authorities seek for applicable
solutions through improving the operational processes of key stakeholders such
as ship operators, shipyards, offshore structures, ports & terminals, etc.
This study reviews the existing data analytics solutions derived by research
groups, classification societies, and technology providers. Considering the
operational level integration requirements, the potential of the existing
solutions in safety, efficiency, environmental sensitiveness and other
performance indicators is determined. In conclusion, this study contributes to
identification of the challenges and opportunities in order to extent data
analytics applications to maritime field.  

References

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  • Bilgili, L., & Celebi, U. B. (2016). Emission Routing in Maritime Transportation. In Energy, Transportation and Global Warming (pp. 837-849). Springer International Publishing.
  • Coraddu, A., Oneto, L., Baldi, F., & Anguita, D. (2017). Vessels fuel consumption forecast and trim optimisation: A data analytics perspective. Ocean Engineering, 130, 351-370.
  • Dobrkovic, A., Iacob, M. E., & van Hillegersberg, J. (2015, October). Using machine learning for unsupervised maritime waypoint discovery from streaming AIS data. In Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business (p. 16). ACM.
  • E. Brynjolfsson, L. M. Hitt, and H. H. Kim, "Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance?" SSRN Electronic Journal, 2011.
  • EU, 2014. Proposal for a Regulation of the European Parliament and of the Council on the Monitoring, Reporting and Verification of Carbon Dioxide Emissions From Maritime Transport and Amending Regulation (EU) No. 525/2013–Political Agreement. Available at: 〈 http://register.consilium.europa.eu/doc/srv?l=EN&f=ST%2016238%202014%20INIT〉.
  • Giannakopoulos, T., Vetsikas, I. A., Koromila, I., Karkaletsis, V., & Perantonis, S. (2014, May). Aminess: a platform for environmentally safe shipping. In Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments (p. 45). ACM.
  • IALA. The IALA definition and vision for e-Navigation. E-NAV2-output 11 (March 2007) IHO, S. (2010). 100, Universal Hydrographic Data Model. Robert Ward, Barrie Greenslad.
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  • Koga, S. (2015). Major challenges and solutions for utilizing big data in the maritime industry.
  • Leonardi, J., & Browne, M. (2010). A method for assessing the carbon footprint of maritime freight transport: European case study and results. International Journal of Logistics: research and applications, 13(5), 349-358.
  • Løvoll, G., & Kadal, J. C. (2014). Big data-the new data reality and industry impact. DNV GL.
  • Mak, L., Seo, D. C., Kuczora, A., & Sullivan, M. (2015, May). Vessel Performance Analysis and Fuel Management. In ASME 2015 34th International Conference on Ocean, Offshore and Arctic Engineering (pp. V011T12A052-V011T12A052). American Society of Mechanical Engineers.
  • Perera, L. P. (2016). Statistical Filter based Sensor and DAQ Fault Detection for Onboard Ship Performance and Navigation Monitoring Systems. IFAC-PapersOnLine, 49(23), 323-328.
  • Perera, L. P., & Mo, B. (2016-a). Data analysis on marine engine operating regions in relation to ship navigation. Ocean Engineering, 128, 163-172.
  • Perera, L. P., & Mo, B. (2016-b). Marine Engine Operating Regions under Principal Component Analysis to evaluate Ship Performance and Navigation Behavior. IFAC-PapersOnLine, 49(23), 512-517.
  • Perera, L. P., & Mo, B. (2016-c). Emission control based energy efficiency measures in ship operations. Applied Ocean Research, 60, 29-46.
  • Rødseth, Ø. J., Perera, L. P., & Mo, B. (2016). Big data in shipping-Challenges and opportunities. In Proceedings of the 15th International Conference on Computer and IT Applications in the Maritime Industries (COMPIT 2016), Lecce, Italy, May 2016, (361-373).
  • Wang, H., Osen, O. L., Li, G., Li, W., Dai, H. N., & Zeng, W. (2015, November). Big data and industrial internet of things for the maritime industry in northwestern Norway. In TENCON 2015-2015 IEEE Region 10 Conference (pp. 1-5). IEEE.
Year 2017, Volume: 5 Issue: 1, 67 - 70, 30.06.2017
https://doi.org/10.17261/Pressacademia.2017.573

Abstract

References

  • Ando, H. (2014). Smart Ship Application Platform Project (SSAP Project).
  • Baldi, F., Johnson, H., Gabrielii, C., & Andersson, K. (2014). Energy analysis of ship energy systems–the case of a chemical tanker. Energy Procedia, 61, 1732-1735.
  • Bilgili, L., & Celebi, U. B. (2016). Emission Routing in Maritime Transportation. In Energy, Transportation and Global Warming (pp. 837-849). Springer International Publishing.
  • Coraddu, A., Oneto, L., Baldi, F., & Anguita, D. (2017). Vessels fuel consumption forecast and trim optimisation: A data analytics perspective. Ocean Engineering, 130, 351-370.
  • Dobrkovic, A., Iacob, M. E., & van Hillegersberg, J. (2015, October). Using machine learning for unsupervised maritime waypoint discovery from streaming AIS data. In Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business (p. 16). ACM.
  • E. Brynjolfsson, L. M. Hitt, and H. H. Kim, "Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance?" SSRN Electronic Journal, 2011.
  • EU, 2014. Proposal for a Regulation of the European Parliament and of the Council on the Monitoring, Reporting and Verification of Carbon Dioxide Emissions From Maritime Transport and Amending Regulation (EU) No. 525/2013–Political Agreement. Available at: 〈 http://register.consilium.europa.eu/doc/srv?l=EN&f=ST%2016238%202014%20INIT〉.
  • Giannakopoulos, T., Vetsikas, I. A., Koromila, I., Karkaletsis, V., & Perantonis, S. (2014, May). Aminess: a platform for environmentally safe shipping. In Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments (p. 45). ACM.
  • IALA. The IALA definition and vision for e-Navigation. E-NAV2-output 11 (March 2007) IHO, S. (2010). 100, Universal Hydrographic Data Model. Robert Ward, Barrie Greenslad.
  • International Convention for the Safety of Life at Sea, 1974. IMO. (1974).
  • Koga, S. (2015). Major challenges and solutions for utilizing big data in the maritime industry.
  • Leonardi, J., & Browne, M. (2010). A method for assessing the carbon footprint of maritime freight transport: European case study and results. International Journal of Logistics: research and applications, 13(5), 349-358.
  • Løvoll, G., & Kadal, J. C. (2014). Big data-the new data reality and industry impact. DNV GL.
  • Mak, L., Seo, D. C., Kuczora, A., & Sullivan, M. (2015, May). Vessel Performance Analysis and Fuel Management. In ASME 2015 34th International Conference on Ocean, Offshore and Arctic Engineering (pp. V011T12A052-V011T12A052). American Society of Mechanical Engineers.
  • Perera, L. P. (2016). Statistical Filter based Sensor and DAQ Fault Detection for Onboard Ship Performance and Navigation Monitoring Systems. IFAC-PapersOnLine, 49(23), 323-328.
  • Perera, L. P., & Mo, B. (2016-a). Data analysis on marine engine operating regions in relation to ship navigation. Ocean Engineering, 128, 163-172.
  • Perera, L. P., & Mo, B. (2016-b). Marine Engine Operating Regions under Principal Component Analysis to evaluate Ship Performance and Navigation Behavior. IFAC-PapersOnLine, 49(23), 512-517.
  • Perera, L. P., & Mo, B. (2016-c). Emission control based energy efficiency measures in ship operations. Applied Ocean Research, 60, 29-46.
  • Rødseth, Ø. J., Perera, L. P., & Mo, B. (2016). Big data in shipping-Challenges and opportunities. In Proceedings of the 15th International Conference on Computer and IT Applications in the Maritime Industries (COMPIT 2016), Lecce, Italy, May 2016, (361-373).
  • Wang, H., Osen, O. L., Li, G., Li, W., Dai, H. N., & Zeng, W. (2015, November). Big data and industrial internet of things for the maritime industry in northwestern Norway. In TENCON 2015-2015 IEEE Region 10 Conference (pp. 1-5). IEEE.
There are 20 citations in total.

Details

Journal Section Articles
Authors

Omer Soner This is me

Metin Celik This is me

Publication Date June 30, 2017
Published in Issue Year 2017 Volume: 5 Issue: 1

Cite

APA Soner, O., & Celik, M. (2017). INVESTIGATING THE POTENTIAL OF DATA ANALYTICS SOLUTIONS IN MARITIME INDUSTRY. PressAcademia Procedia, 5(1), 67-70. https://doi.org/10.17261/Pressacademia.2017.573
AMA Soner O, Celik M. INVESTIGATING THE POTENTIAL OF DATA ANALYTICS SOLUTIONS IN MARITIME INDUSTRY. PAP. June 2017;5(1):67-70. doi:10.17261/Pressacademia.2017.573
Chicago Soner, Omer, and Metin Celik. “INVESTIGATING THE POTENTIAL OF DATA ANALYTICS SOLUTIONS IN MARITIME INDUSTRY”. PressAcademia Procedia 5, no. 1 (June 2017): 67-70. https://doi.org/10.17261/Pressacademia.2017.573.
EndNote Soner O, Celik M (June 1, 2017) INVESTIGATING THE POTENTIAL OF DATA ANALYTICS SOLUTIONS IN MARITIME INDUSTRY. PressAcademia Procedia 5 1 67–70.
IEEE O. Soner and M. Celik, “INVESTIGATING THE POTENTIAL OF DATA ANALYTICS SOLUTIONS IN MARITIME INDUSTRY”, PAP, vol. 5, no. 1, pp. 67–70, 2017, doi: 10.17261/Pressacademia.2017.573.
ISNAD Soner, Omer - Celik, Metin. “INVESTIGATING THE POTENTIAL OF DATA ANALYTICS SOLUTIONS IN MARITIME INDUSTRY”. PressAcademia Procedia 5/1 (June 2017), 67-70. https://doi.org/10.17261/Pressacademia.2017.573.
JAMA Soner O, Celik M. INVESTIGATING THE POTENTIAL OF DATA ANALYTICS SOLUTIONS IN MARITIME INDUSTRY. PAP. 2017;5:67–70.
MLA Soner, Omer and Metin Celik. “INVESTIGATING THE POTENTIAL OF DATA ANALYTICS SOLUTIONS IN MARITIME INDUSTRY”. PressAcademia Procedia, vol. 5, no. 1, 2017, pp. 67-70, doi:10.17261/Pressacademia.2017.573.
Vancouver Soner O, Celik M. INVESTIGATING THE POTENTIAL OF DATA ANALYTICS SOLUTIONS IN MARITIME INDUSTRY. PAP. 2017;5(1):67-70.

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