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An architecture modeling framework for probabilistic prediction

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Abstract

In the design phase of business and IT system development, it is desirable to predict the properties of the system-to-be. A number of formalisms to assess qualities such as performance, reliability and security have therefore previously been proposed. However, existing prediction systems do not allow the modeler to express uncertainty with respect to the design of the considered system. Yet, in contemporary business, the high rate of change in the environment leads to uncertainties about present and future characteristics of the system, so significant that ignoring them becomes problematic. In this paper, we propose a formalism, the Predictive, Probabilistic Architecture Modeling Framework (P2AMF), capable of advanced and probabilistically sound reasoning about business and IT architecture models, given in the form of Unified Modeling Language class and object diagrams. The proposed formalism is based on the Object Constraint Language (OCL). To OCL, P2AMF adds a probabilistic inference mechanism. The paper introduces P2AMF, describes its use for system property prediction and assessment and proposes an algorithm for probabilistic inference.

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Notes

  1. We use the term framework to denote a formal (system description) language and a set of inference rules (for attribute value assessment).

  2. An early version of P2AMF was published as Probabilistic Imperative Object Constraint Language (Pi-OCL) in 2010 (Ullberg et al. 2010) While the general lines of thought remain the same, the early version was unnecessarily dependent on a formalism that was limited to exact probabilistic inference rather than more general approximate sampling algorithms, and lacked performance evaluation.

  3. Root attributes are attributes that have no causal parents.

  4. The tool is available for download at http://www.ics.kth.se/eaat.

  5. MacBook Pro, 2.4 GHz Intel Core i5, 4 GB RAM.

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Authors and Affiliations

  1. Department of Industrial Information and Control Systems, KTH – Royal Institute of Technology, 100 44, Stockholm, Sweden

    Pontus Johnson, Johan Ullberg, Markus Buschle & Khurram Shahzad

  2. FOI – Swedish Defence Research Agency, 164 90, Stockholm, Sweden

    Ulrik Franke

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  1. Pontus Johnson
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  2. Johan Ullberg
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  4. Ulrik Franke
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  5. Khurram Shahzad
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Correspondence to Johan Ullberg.

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Johnson, P., Ullberg, J., Buschle, M. et al. An architecture modeling framework for probabilistic prediction. Inf Syst E-Bus Manage 12, 595–622 (2014). https://doi.org/10.1007/s10257-014-0241-8

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