A Methodology for Quality-Based Selection of Internet Data Sources in Maritime Domain

  • Milena StróżynaEmail author
  • Gerd Eiden
  • Dominik Filipiak
  • Jacek Małyszko
  • Krzysztof Węcel
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 255)


The paper presents a methodology for identification, assessment and selection of internet data sources that shall be used to supplement existing internal data in a continuous manner. Several criteria are specified to help in the selection process. The proposed method is described based on an example of the system for the maritime surveillance purposes, originally developed within the SIMMO research project. As a result, we also present a ranking of concrete data sources. The presented methodology is universal and can be applied to other domains, where internet sources can offer additional data.


Internet data sources Quality assessment Selection methodology 



This work was supported by a grant provided for the project SIMMO: System for Intelligent Maritime MOnitoring (contract no. A-1341-RT-GP), financed by the Contributing Members of the JIP-ICET 2 Programme and supervised by the European Defence Agency.


  1. 1.
    Robey, D., Markus, M.L.: Rituals in information system design. MIS Q. 8, 5–15 (1984)CrossRefGoogle Scholar
  2. 2.
    International Organization for Standardization: ISO 8402–1986 (GB/T6583-1992): Quality-Vocabulary, June 1986Google Scholar
  3. 3.
    Vespe, M., Sciotti, M., Battistello, G.: Multi-sensor autonomous tracking for maritime surveillance. In: International Conference on Radar, 2008, pp. 525–530. IEEE (2008)Google Scholar
  4. 4.
    European Commission: Integrated Maritime Policy for the EU. Working document III on Maritime Surveillance Systems (2008)Google Scholar
  5. 5.
    Kazemi, S., Abghari, S., Lavesson, N., Johnson, H., Ryman, P.: Open data for anomaly detection in maritime surveillance. Expert Syst. Appl. 40(14), 5719–5729 (2013)CrossRefGoogle Scholar
  6. 6.
    Alonso, J., Ambur, O., Amutio, M.A., Azañón, O., Bennett, D., Flagg, R., McAllister, D., Novak, K., Rush, S., Sheridan, J.: Improving access to government through better use of the web. World Wide Web Consortium (2009)Google Scholar
  7. 7.
    Rhodes, B.J., Bomberger, N.A., Seibert, M., Waxman, A.M.: Maritime situation monitoring and awareness using learning mechanisms. In: Military Communications Conference, MILCOM 2005, pp. 646–652. IEEE (2005)Google Scholar
  8. 8.
    Fooladvandi, F., Brax, C., Gustavsson, P., Fredin, M.: Signature-based activity detection based on Bayesian networks acquired from expert knowledge. In: 12th International Conference on Information Fusion, FUSION 2009, pp. 436–443. IEEE (2009)Google Scholar
  9. 9.
    Riveiro, M., Falkman, G., Ziemke, T.: Improving maritime anomaly detection and situation awareness through interactive visualization. In: 11th International Conference on Information Fusion, 2008, pp. 1–8. IEEE (2008)Google Scholar
  10. 10.
    Helldin, T., Riveiro, M.: Explanation methods for Bayesian networks: review and application to a maritime scenario. In: Proceedings of the 3rd Annual Skövde Workshop on Information Fusion Topics (SWIFT 2009), pp. 11–16 (2009)Google Scholar
  11. 11.
    Peter, B.: Data quality. The key to interoperability (2010)Google Scholar
  12. 12.
    Wang, R.Y., Reddy, M.P., Kon, H.B.: Toward quality data: an attribute-based approach. Decis. Support Syst. 13(3), 349–372 (1995)CrossRefGoogle Scholar
  13. 13.
    Wang, R.Y., Strong, D.M.: Beyond accuracy: what data quality means to data consumers. J. Manage. Inf. Syst. 12, 5–33 (1996)CrossRefGoogle Scholar
  14. 14.
    Batini, C., Cappiello, C., Francalanci, C., Maurino, A.: Methodologies for data quality assessment and improvement. ACM Comput. Surv. 41(3), 16:1–16:52 (2009)CrossRefGoogle Scholar
  15. 15.
    European Statistical System: ESS handbook for quality reports. Eurostat (2014)Google Scholar
  16. 16.
    European Parliament: Regulation (EC) No 223/2009 of the European Parliament and the Council of 11 on European statistics and repealing Regulation (EC, Euratom). Official J. Eur. Union 52 (2009)Google Scholar
  17. 17.
    Naumann, F., Freytag, J.C., Spiliopoulou, M.: Quality-driven source selection using data envelopment analysis. In: Proceedings of the 3rd Conference on Information Quality (IQ), Cambridge, MA (1998)Google Scholar
  18. 18.
    Dorofeyuk, A., Pokrovskaya, I., Chernyavkii, A.: Expert methods to analyze and perfect management systems. Autom. Remote Control 65(10), 1675–1688 (2004)CrossRefzbMATHGoogle Scholar
  19. 19.
    Kazemi, S., Abghari, S., Lavesson, N., Johnson, H., Ryman, P.: Open data for anomaly detection in maritime surveillance. Expert Syst. Appl. 40(14), 5719–5729 (2013)CrossRefGoogle Scholar
  20. 20.
    Brown, B.B.: Delphi process: a methodology used for the elicitation of opinions of experts. Technical report, DTIC Document (1968)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Milena Stróżyna
    • 1
    Email author
  • Gerd Eiden
    • 2
  • Dominik Filipiak
    • 1
  • Jacek Małyszko
    • 1
  • Krzysztof Węcel
    • 1
  1. 1.Poznań University of Economics and BusinessPoznańPoland
  2. 2.LuxSpace SarlBetzdorfLuxembourg

Personalised recommendations