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Modeling collaboration preparedness assessment

  • J. Rosas
  • L. M. Camarinha-Matos

Information incompleteness and imprecision are typical difficulties when assessing the collaboration preparedness of a candidate to join a collaborative network. Bayesian belief networks and Rough Sets are examples of modeling approaches that can be used in these cases. The use of these approaches depends on the type of collaborative network considered, namely long term or goal oriented, and on the available data necessary to perform the assessment. Combination of different modeling techniques is also useful in this context. In order to illustrate the suggested approach, a number of modeling experiments are described and achieved results are briefly discussed.

Keywords

Virtual Organization Collaborative Network Bayesian Belief Network Preparedness Level Virtual Organization Breeding Environment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Afsarmanesh H, Camarinha-Matos LM. A framework for management of virtual organization breeding environments. In: Collaborative Networks and their Breeding Environments; eds. Camarinha-Matos, L., Afsarmanesh, Ortiz, A.;( Springer), pp. 35-48, 2005.Google Scholar
  2. Baldo F, Rabelo RJ, Vallejos RV. An Ontology-based Approach for selecting performance indicators for partners suggestion. In Establishing the Foundation of Collaborative 07), Springer, Guimaraes, Portugal, 10-12 Sep 2007.Google Scholar
  3. Camarinha-Matos LM, Silveri I, Afsarmanesh H, Oliveira AI. Towards a Framework for creation of Dynamic Virtual Organizations. In: Collaborative Networks and their Breeding Environments; eds. Camarinha-Matos, L., Afsarmanesh, Ortiz, A.;( Springer), pp. 26-28, 2005.Google Scholar
  4. Camarinha-Matos LM, Afsarmanesh H. Creation of Virtual Organizations in a Breeding 06 - St. Etienne, France - 17-19 May 2006.Google Scholar
  5. Cheng J, Greiner R. Learning Bayesian Belief Network Classifiers: Algorithms and System. In: Lecture Notes in Computer Science, page 141-151, vol. 2056, 2001.Google Scholar
  6. Friedman N. Learning belief networks in the presence of missing values and hidden variables. In: D. Fisher, ed., Proceedings of the Fourteenth International Conference on Machine Learning, Morgan Kaufmann, San Francisco, CA, pp. 125-133, 1997.Google Scholar
  7. Hassanien AE. Rough Set Approach for Attribute Reduction and Rule Generation: A Case of Patients With Suspected Breast Cancer. In: Journal of the American Society for information Science and Technology, 55(11): 954-962, 2004.Google Scholar
  8. Jarimo T, Ljubi P, Salkari I, Bohanec M, Lavra N, ŽnidarŽi M, Bollhalter S, Hodik J. Hierarchical multi-attribute decision support approach to virtual organization creation. In: Collaborative Networks and their Breeding Environments; eds. Camarinha-Matos, L., Afsarmanesh, Ortiz, A.;( Springer), pp. 135-142, 2005.Google Scholar
  9. Jensen, FV. Bayesian Networks basics. In: AISB Quarterly, 94:9-22, 1996.Google Scholar
  10. Komorowski J, Øhrn A, Skowron A. The ROSETTA Rough Set Software System, In: Handbook of Data Mining and Knowledge Discovery, W. Klösgen and J. Zytkow (eds.), ch. D.2.3, Oxford University Press. ISBN 0-19-511831-6, 2002.Google Scholar
  11. Martinich JS. Production and Operations Management: an applied modern approach, John Wiley & Sons, 1997.Google Scholar
  12. Netica application for Belief Networks and Influence Diagrams Users Guide, Norsys Software Group, 1997,http://www.norsys.com.
  13. Pawlak Z. Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic, 1991.Google Scholar
  14. Pawlak Z. Vagueness and Uncertainty: a Rough Set Perspective. In: Computational Intelligence 11: pp. 277-232, 1995.Google Scholar
  15. Pawlak Z, Skowron A. Rough Sets Rudiments. In: Bulletin of IRSS 3/3, pp 67-70, 1999.Google Scholar
  16. Pearl J. Decision Making Under Uncertainty. In: ACM Computing Surveys, Vol. 28, No. 1, March 1996.Google Scholar
  17. Wang Y, Vassileva J. Bayesian Network-Based Trust Model in Peer-to-Peer Networks. In: Proc. Workshop on Deception, Fraud and Trust in Agent Societies at the Autonomous Agents and Multi Agent Systems 2003 (AAMAS-03), Melbourne, Australia, July 2003.Google Scholar

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© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • J. Rosas
  • L. M. Camarinha-Matos

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