Towards Characterizing Distributed Complex Situation Assessment as Workflows in Loosely Coupled Systems

  • Costin Bădică
  • Claudine Conrado
  • Franck Mignet
  • Patrick de Oude
  • Gregor Pavlin
Part of the Studies in Computational Intelligence book series (SCI, volume 446)


This paper introduces challenges in contemporary situation assessment using collaborative inference and discusses solutions that are based on workflows between distributed processing nodes. The paper exposes the necessary conditions that workflows have to satisfy in order to support accurate situation assessment and provides a systematic approach to verification of the workflows. In particular, we emphasize the link between the complexity of the domain and the complexity of the workflows in terms of data and control coupling. With the help of graphical representations, we characterize the complexity of the domains and identify critical relations that have to be captured by collaborating processes in a workflow supporting correct situation assessment.


Domain Structure Bayesian Network Population Distribution Lyme Disease Causal Process 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bhatt, E., Fujimoto, R., Ogielski, A., Perumalla, K.: Parallel simulation techniques for large scale networks. IEEE Communications Magazine 36, 42–47 (2002)CrossRefGoogle Scholar
  2. 2.
    Chandrasekaran, B.: Functional representation and causal processes. Advances in Computers 38, 73–143 (1994)CrossRefGoogle Scholar
  3. 3.
    Deelman, E., Szymanski, B.K., Caraco, T.: Simulating lyme disease using parallel discrete event simulation. In: Proceedings of the Winter Simulation Conference, pp. 1191–1198 (1996)Google Scholar
  4. 4.
    Forbus, K.D.: Qualitative physics: Past, present, and future. In: Exploring Artificial Intelligence, ch. 7, pp. 239–296. Morgan-Kaufmann Publishers, Inc., San Francisco (1988)Google Scholar
  5. 5.
    Jensen, F.V., Nielsen, T.D.: Bayesian Networks and Decision Graphs, 2nd edn. Springer (2007)Google Scholar
  6. 6.
    Karimabadi, H., Driscoll, J., Dave, J., Omelchenko, Y., Perumalla, K., Fujimoto, R., Omidi, N.: Parallel Discrete Event Simulations of Grid-Based Models: Asynchronous Electromagnetic Hybrid Code. In: Dongarra, J., Madsen, K., Waśniewski, J. (eds.) PARA 2004. LNCS, vol. 3732, pp. 573–582. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Kiepuszewski, B., ter Hofstede, A.H.M., van der Aalst, W.M.P.: Fundamentals of control flow in workflows. Acta Informatica 39(3), 143–209 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Kreps, G.A., Bosworth, S.L.: Organizational adaptation to disaster. In: Handbook of Disaster Research, ch. 17, pp. 297–315. Springer (2007)Google Scholar
  9. 9.
    Kuipers, B.: Qualitative simulation. Artificial Intelligence 29, 289–338 (2001)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Lauritzen, S.L., Spiegelhalter, D.J.: Local computations with probabilities on graphical structures and their application to expert systems, pp. 415–448 (1990)Google Scholar
  11. 11.
    Lukovszki, C., Szabó, R., Henk, T.: Performance evaluation of a hybrid atm switch architecture by parallel discrete event simulation. Informatica (Slovenia) 24(2) (2000)Google Scholar
  12. 12.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann (1988)Google Scholar
  13. 13.
    Pearl, J.: Causality: Models Reasoning and Inference. Cambridge University Press (2000)Google Scholar
  14. 14.
    Penders, A., Pavlin, G., Kamermans, M.: A collaborative approach to construction of large scale distributed reasoning systems. International Journal on Artificial Intelligence Tools 20(6), 1083–1106 (2011)CrossRefGoogle Scholar
  15. 15.
    Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search, 2nd edn. MIT Press (2000)Google Scholar
  16. 16.
    van der Aalst, W.: Workflow Management: Models, Methods, and Systems. MIT Press (2002)Google Scholar
  17. 17.
    Wieland, F.: Parallel simulation for aviation applications. In: Winter Simulation Conference, pp. 1191–1198 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Costin Bădică
    • 1
  • Claudine Conrado
    • 2
  • Franck Mignet
    • 2
  • Patrick de Oude
    • 2
  • Gregor Pavlin
    • 2
  1. 1.Software Engineering Department, Faculty of Automatics, Computers and ElectronicsUniversity of CraiovaCraiovaRomania
  2. 2.D-CIS LabThales Research & Technology NetherlandsDelftThe Netherlands

Personalised recommendations