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International Conference on Conceptual Modeling

ER 2014: Conceptual Modeling pp 466–473Cite as

Creating Quantitative Goal Models: Governmental Experience

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8824))

Abstract

Precision in goal models can be enhanced using quantitative rather than qualitative scales. Selecting appropriate values is however often difficult, especially when groups of stakeholders are involved. This paper identifies and compares generic and domain-specific group decision approaches for selecting quantitative values in goal models. It then reports on the use of two approaches targeting quantitative contributions, actor importance, and indicator definitions in the Goal-oriented Requirement Language. The approaches have been deployed in two independent branches of the Canadian government.

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Author information

Authors and Affiliations

  1. University of Ottawa, Canada

    Okhaide Akhigbe, Mohammad Alhaj, Daniel Amyot, Edna Braun, Nick Cartwright & Gregory Richards

  2. Northern Arizona University, USA

    Omar Badreddin

  3. McGill University, Canada

    Gunter Mussbacher

Authors
  1. Okhaide Akhigbe
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  2. Mohammad Alhaj
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  3. Daniel Amyot
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  4. Omar Badreddin
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  5. Edna Braun
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  6. Nick Cartwright
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  7. Gregory Richards
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  8. Gunter Mussbacher
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Editor information

Editors and Affiliations

  1. Faculty of Information, University of Toronto, 140 St. George St., M5S 3G6, Toronto, ON, Canada

    Eric Yu

  2. Dept of Computer Science, University of Auckland, Private Bag 92019, 1142, Auckland, New Zealand

    Gillian Dobbie

  3. Lehrstuhl Informatik 5, RWTH Aachen University, Ahornstr. 55, 52056, Aachen, Germany

    Matthias Jarke

  4. Penn State University, 316B Information Sciences and Technology Building, 16802, University Park, PA, USA

    Sandeep Purao

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© 2014 Springer International Publishing Switzerland

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Akhigbe, O. et al. (2014). Creating Quantitative Goal Models: Governmental Experience. In: Yu, E., Dobbie, G., Jarke, M., Purao, S. (eds) Conceptual Modeling. ER 2014. Lecture Notes in Computer Science, vol 8824. Springer, Cham. https://doi.org/10.1007/978-3-319-12206-9_40

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  • DOI: https://doi.org/10.1007/978-3-319-12206-9_40

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12205-2

  • Online ISBN: 978-3-319-12206-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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