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Parametric Stochastic Well-Formed Nets and Compositional Modelling

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Application and Theory of Petri Nets 2000 (ICATPN 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1825))

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Abstract

Colored nets have been recognized as a powerful modelling paradigm for the validation and evaluation of systems, both in terms of compact representation and aggregate state space generation. In this paper we discuss the issue of adding compositionality to a class of stochastic colored nets named Stochastic Well-formed Nets, in order to increase modularity and reuse of the modelling efforts. This requires the notion of Parametric Stochastic Well-formed net: nets in which a certain amount of information is left unspecified, and is instantiated only upon model composition. The choice of the compositional rule has been based on previous work on layered models for integrated hardware and software systems (the processes, services and resources methodology), and an example of layered modelling with Parametric Stochastic Well-formed net is presented to show the efficacy of the proposed formalism.

We acknowledge contribution of the EEC project 28620 TIRAN.

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Author information

Authors and Affiliations

  1. Dip.to di Informatica, Università di Torino, Italy

    Paolo Ballarini & Susanna Donatelli

  2. DSTA, Università del Piemonte Orientale, Alessandria, Italy

    Giuliana Franceschinis

Authors
  1. Paolo Ballarini
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  2. Susanna Donatelli
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  3. Giuliana Franceschinis
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Aarhus, Ny Munkegade bldg 540, 8000, Aarhus C, Denmark

    Mogens Nielsen

  2. University of Brighton, Watts Building Lewes Road, Brighton, East Sussex, BN2 4GJ, UK

    Dan Simpson

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© 2000 Springer-Verlag Berlin Heidelberg

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Ballarini, P., Donatelli, S., Franceschinis, G. (2000). Parametric Stochastic Well-Formed Nets and Compositional Modelling. In: Nielsen, M., Simpson, D. (eds) Application and Theory of Petri Nets 2000. ICATPN 2000. Lecture Notes in Computer Science, vol 1825. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44988-4_5

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  • DOI: https://doi.org/10.1007/3-540-44988-4_5

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67693-5

  • Online ISBN: 978-3-540-44988-1

  • eBook Packages: Springer Book Archive

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