Revealing Complexity through Domain-Specific Modelling and Analysis

  • Richard F. Paige
  • Phillip J. Brooke
  • Xiaocheng Ge
  • Christopher D. S. Power
  • Frank R. Burton
  • Simon Poulding
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7539)


Complex systems exhibit emergent behaviour. The explanations for this explicit emergent behaviour are often difficult to identify, and usually require understanding of significant parts of system structure and component behaviour to interpret. We present ongoing work on a set of techniques, based on Model-Driven Engineering principles and practices, for helping to reveal explanations for system complexity. We outline the techniques abstractly, and then illustrate parts of them with three examples from the health care, system security and Through-Life Capability Management domains.


Business Process Model Checker Model Transformation Transient Ischaemic Attack Failure Behaviour 
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.
    Baxter, G.: White paper: Complexity in health care. Technical report, Large Scale Complex IT System, LSCITS (2010)Google Scholar
  2. 2.
    Brook, R.H., McGlynn, E.A., Cleary, P.D.: Quality of health care: measuring quality of care. New England Journal of Medicine 335, 966–970 (1996)CrossRefGoogle Scholar
  3. 3.
    Brooke, P.J., Paige, R.F., Power, C.: State exploration and property checking for fuzzy scenarios (under review, 2012)Google Scholar
  4. 4.
    Burton, F.R., Paige, R.F., Rose, L.M., Kolovos, D.S., Poulding, S., Smith, S.: Solving Acquisition Problems Using Model-Driven Engineering. In: Vallecillo, A., Tolvanen, J.-P., Kindler, E., Störrle, H., Kolovos, D. (eds.) ECMFA 2012. LNCS, vol. 7349, pp. 428–443. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Donabedian, A.: The Definition of Quality and Approaches to Its Assessment. Health Administration Press (1980)Google Scholar
  6. 6.
    dos Santos, O.M., Woodcock, J., Paige, R.F.: Using model transformation to generate graphical counter-examples for the formal analysis of xuml models. In: ICECCS, pp. 117–126 (2011)Google Scholar
  7. 7.
    Object Management Group. Business process definition metamodel (BPDM), process definitions (2008),
  8. 8.
    Haywood-Farmer, J., Alleyne, A., Duffus, B., Downing, M.: Controlling service quality. Business Quarlerly 50(4), 62–67 (1986)Google Scholar
  9. 9.
    Haywood-Farmer, J.: A conceptual model of service quality. International Journal of Operations and Production Management 8(6), 19–29 (1988)CrossRefGoogle Scholar
  10. 10.
    Kwiatkowska, M., Norman, G., Parker, D.: PRISM: Probabilistic Symbolic Model Checker. In: Field, T., Harrison, P.G., Bradley, J., Harder, U. (eds.) TOOLS 2002. LNCS, vol. 2324, pp. 113–140. Springer, Heidelberg (2002)Google Scholar
  11. 11.
    Lowe, G.: Probabilistic and prioritized models of timed CSP. Theoretical Computer Science 13(2), 315–352 (1995)MathSciNetCrossRefGoogle Scholar
  12. 12.
    McKane, T.: Enabling acquisition change - an examination of the Ministry of Defence’s ability to undertake Through Life Capability Management. Technical report (June 2006)Google Scholar
  13. 13.
    NHS. Acute stroke and transient ischaemic attack suspected (January 2010),
  14. 14.
    Parasuraman, A., Zeithaml, V.A., Berry, L.L.: A conceptual model of service quality and its implications for future research. Journal of Marketing 49, 41–50 (1985)CrossRefGoogle Scholar
  15. 15.
    Plsek, P.E., Greenhalgh, T.: The challenge of complexity in health care. British Medical Journal 323, 624–628 (2001)Google Scholar
  16. 16.
    Sweeney, K., Griffiths, F. (eds.): Complexity and Healthcare: an introduction. Radcliffe Medical Press (2002)Google Scholar
  17. 17.
    Wallace, M.: Modular architectural representation and analysis of fault propagation and transformation. Electr. Notes Theor. Comput. Sci. 141(3), 53–71 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Richard F. Paige
    • 1
  • Phillip J. Brooke
    • 2
  • Xiaocheng Ge
    • 1
  • Christopher D. S. Power
    • 1
  • Frank R. Burton
    • 1
  • Simon Poulding
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkYorkUK
  2. 2.School of ComputingTeesside UniversityMiddlesbroughUK

Personalised recommendations