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An architecture modeling framework for probabilistic prediction
KTH, Skolan för elektro- och systemteknik (EES), Industriella informations- och styrsystem.ORCID-id: 0000-0002-3293-1681
KTH, Skolan för elektro- och systemteknik (EES), Industriella informations- och styrsystem.
KTH, Skolan för elektro- och systemteknik (EES), Industriella informations- och styrsystem.
FOI - Swedish Defence Research Agency, Sweden.ORCID-id: 0000-0003-2017-7914
Vise andre og tillknytning
2014 (engelsk)Inngår i: Information Systems and E-Business Management, ISSN 1617-9846, E-ISSN 1617-9854, Vol. 12, nr 4, s. 595-622Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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 (P(2)AMF), 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, P(2)AMF adds a probabilistic inference mechanism. The paper introduces P(2)AMF, describes its use for system property prediction and assessment and proposes an algorithm for probabilistic inference.

sted, utgiver, år, opplag, sider
2014. Vol. 12, nr 4, s. 595-622
Emneord [en]
Assessment, Business properties, Object Constraint Language, Prediction, Probabilistic inference, System properties, UML
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-149177DOI: 10.1007/s10257-014-0241-8ISI: 000344741500006Scopus ID: 2-s2.0-84912040897OAI: oai:DiVA.org:kth-149177DiVA, id: diva2:738217
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QC 20141212

Tilgjengelig fra: 2014-08-16 Laget: 2014-08-16 Sist oppdatert: 2017-12-05bibliografisk kontrollert

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