Advanced services for process evolution: Monitoring and decision support

  • Ilham Alloui
  • Sami Beydeda
  • Sorana Cîmpan
  • Volker Gruhn
  • Flavio Oquendo
  • Christian Schneider
Session 0: PIE Workshop
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1780)


Process support environments (PSEs) are widely used for modelling, enacting and analyzing human intensive processes. The benefits of a PSE become apparent when processes to be supported are long lived and distributed and contain heterogeneous components. Generally, such processes are subject to dynamic evolution, i.e. they have to be changed during their execution. Unfortunately, virtually none of the existing PSEs consider dynamic evolution of processes. This article explains the concepts and techniques underlying a set of components developed in the ESPRIT Project Process Instance Evolution (PIE) that support the dynamic evolution of processes. These concepts and techniques are demonstrated using a real-world scenario from the automotive industry.


Risk Measure Process Manager Fuzzy Subset Conformance Factor Process Instance 
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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  1. 1.
    Allen, A.O.: Probability, Statistics, and Queueing Theory. Academic Press, New York San Francisco London (1978)zbMATHGoogle Scholar
  2. 2.
    Alloui, I., Cimpan, S., Oquendo, F., Verjus, H.: Tuning a Fuzzy Control System for Software Intensive Processes via Simulations. Proceedings of the IASTED International Conference on Modeling and Simulation, Philadelphia PA, USA, May 5–8 (1999)Google Scholar
  3. 3.
    Alloui, I., Cimpan, S., Oquendo, F., Verjus, H.: A Fuzzy Sets based Mechanism Allowing the Tuning of a Software Intensive Processes Control System via Multiple Simulations. Proceedings of the AMSE International Conference on Modelling and Simulation MS'99, Santiago de Compostela, May 17–19 (1999)Google Scholar
  4. 4.
    Arrow, K.J.: Social Choice and Individual Values, Wiley, New York (1963)zbMATHGoogle Scholar
  5. 5.
    Basili, V.R., Caldiera, G., Rombach, H.D.: The Goal Question Metric Approach. Encyclopedia of Software Engineering, Wiley (1994)Google Scholar
  6. 6.
    Bouchon-Meunier, B.: La logique Floue et ses applications. Addison Wesley France, ISBN: 2-87908-073-8, Paris, France (1995)Google Scholar
  7. 7.
    Bradac, M., Perry, D.P., Votta, L.G.: Prototyping a Process Monitoring Experiment. IEEE Transactions on Software Engineering, vol. 20, no. 10 (1994)Google Scholar
  8. 8.
    Cimpan, S., Alloui, I. Oquendo, F.: Process Monitoring Formalism. Technical Report D3.01, PIE LTR ESPRIT Project 34840 (1999)Google Scholar
  9. 9.
    Cimpan, S., Oquendo, F.: On the Application of Fuzzy Sets Theory on the Monitoring of Software-Intensive Processes. Proceedings of the Eight International Fuzzy Systems Association World Congress IFSA'99, Taipei, Taiwan (1999)Google Scholar
  10. 10.
    Cimpan, S., Oquendo, F.: Fuzzy Indicators for Monitoring Software Processes. Proceedings of the 6th European Workshop on Software Process Technology EWSPT'98, Springer Verlag, London, UK (1998)Google Scholar
  11. 11.
    Cook, J., Wolf, A.L.: Toward Metrics for Process Validation. 3rd International Conference on the Software Process, Reston, Virginia, USA (1994)Google Scholar
  12. 12.
    Cook, J.E., Wolf, A.L.: Software Process Validation: Quantitatively Measuring the Correspondence of a Process to a Model. ACM Transactions on Software Engineering and Methodology 8 (2) (1999) 147–176CrossRefGoogle Scholar
  13. 13.
    Cunin, P.Y., The PIE project: An Introduction, accepted for EWSPT'7 (2000)Google Scholar
  14. 14.
    Cunin, P.Y., Dami, S., Auffret J.J.: Refinement of the PIE Workpackages. Technical Report D1.00, PIE LTR ESPRIT Project 34840 (1999)Google Scholar
  15. 15.
    Feiler, P.H., Humphrey, W.S.: Software Process Development and Enactment: Concepts and Definitions. Proceedings of the Second International Conference on the Software Process, February 25–26, Berlin, Germany (1993), 28–40Google Scholar
  16. 16.
    Fenton, N.E.: Software Measurement: A Necessary Scientifiec Basis. IEEE Transactions on Software Engineering, vol. 20, no. 3, pp. 199–206, mars (1994)CrossRefGoogle Scholar
  17. 17.
    Fishburn, P.C., Foundations of Risk Management: Risk as a Probable Loss. Management Science 30 (1984) 396–406zbMATHMathSciNetCrossRefGoogle Scholar
  18. 18.
    Greenwood, M., Robertson, I. and Warboys, B.: A Support Framework for Dynamic Organizations, accepted for EWSPT'7 (2000)Google Scholar
  19. 19.
    Guth, V., Oberweis, A.: Delta-Analysis of Petri Net based Models for Business Processes. In: Kovács, E., Kovács, Z., Csertö, B., Pépei, L. (eds): Proceedings of the 3rd International Conference on Applied Informatics (Eger-Noszvaj, Hungary, August 24–28) (1997) 23–32Google Scholar
  20. 20.
    Hapner, M., Burridge, R., Sharma, R.: Java Message Service. Sun Microsystems, Java Software, Version 1.0.1, October (1998)Google Scholar
  21. 21.
    Humphrey, W.S.: Characterising the Software Process: A maturity framework. IEEE Software, 5(2) (1988) 73–79CrossRefGoogle Scholar
  22. 22.
    Iida, H., Mimura, K., Inoue, K., Torii, K.: Hakoniwa: Monitor and Navigation System for Cooperative Development Based on Activity Sequence Model. Proceedings of 2nd International Conference on Software Process, Los Alamitos, California, IEEE CS Press (1993)Google Scholar
  23. 23.
    Jorion, P.: Value at Risk: A New Benchmark for Measuring Derivatives Risk. Irwin Professional Publishing, Chicago (1997)Google Scholar
  24. 24.
    Lonchamp, J.: A Structured Conceptual and Terminological Framework for Software Process Engineering. Proceedings of the Second International Conference on the Software Process, February 25–26, Berlin, Germany (1993), 41–53Google Scholar
  25. 25.
    Luce, R.D., Several Possible Measures of Risk. Theory and Decision 12 (1980) 217–228zbMATHMathSciNetCrossRefGoogle Scholar
  26. 26.
    Moskowitz, H., Bunn, D.: Decision and Risk Analysis. European Journal of Operational Research 28 (1987) 247–160MathSciNetGoogle Scholar
  27. 27.
    von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behaviour. Princeton University Press, Princeton (1947)Google Scholar
  28. 28.
    PIE Consortium: Process Instance Evolution. Technical Report Annex 1, PIE LTR ESPRIT Project 34840 (1998)Google Scholar
  29. 29.
    Pratt, J., Risk Aversion in the Small and in the Large. Econometrica 32 (1964) 122–136zbMATHCrossRefGoogle Scholar
  30. 30.
    Ramsey, F.P.: Truth and Probability, The Foundations of Mathematics and Other Logical Essays, Harcourt Brace, New York (1931)Google Scholar
  31. 31.
    Selby, R.W., Porter, A.A., Schmidt, D.C., Berney, J.: Metric-Driven Analysis and Feedback Systems for Enabling Empirically Guided Software Development. Proceedings 13th International Conference on Software Engineering. Los Alamitos, California. IEEE CS Press (1991).Google Scholar
  32. 32.
    Zadeh, L.A.: Fuzzy Sets. Information and Control, Vol. 8 (1965)Google Scholar
  33. 33.
    Zadeh, L.A.: Quantitative Fuzzy Semantics. Information Sciences, Vol. 3 (1971)Google Scholar

Copyright information

© Springer-Verlag 2000

Authors and Affiliations

  • Ilham Alloui
    • 1
  • Sami Beydeda
    • 2
  • Sorana Cîmpan
    • 1
  • Volker Gruhn
    • 2
  • Flavio Oquendo
    • 1
  • Christian Schneider
    • 2
  1. 1.ESIA LLPUniversity of Savoie at AnnecyAnnecy CedexFrance
  2. 2.Computer Science Department, Software TechnologyUniversity of DortmundDortmundGermany

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