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Hybrid Probabilistic Relational Models for System Quality Analysis
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.ORCID iD: 0000-0002-3293-1681
2010 (English)In: Proceedings - IEEE International Enterprise Distributed Object Computing Workshop, EDOC, KTH Royal Institute of Technology, 2010, 57-66 p.Conference paper, Published paper (Refereed)
Abstract [en]

The formalism Probabilistic Relational Models (PRM) couples discrete Bayesian Networks with a modeling formalism similar to UML class diagrams and has been used for architecture analysis previously. PRMs are well-suited to perform architecture analysis with respect to system qualities since they support both modeling and analysis at the same time. A particular strength of PRMs is the ability to perform meaningful analysis of domains where there is a high level of uncertainty, as is often the case when performing system quality analysis. However, the use of discrete Bayesian networks in PRMs complicates the analysis of continuous phenomena. The main contribution of this paper is the Hybrid Probabilistic Relational Models (HPRM) formalism which extends PRMs to enable continuous analysis thus extending the applicability for architecture analysis and especially for trade-off analysis of system qualities. HPRMs use hybrid Bayesian networks which allow combinations of discrete and continuous variables. In addition to presenting the HPRM formalism, the paper contains an example which details the use of HPRMs for architecture trade-off analysis.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2010. 57-66 p.
Series
IR-EE-ICS_, 2010:080
Keyword [en]
Hybrid Probabilistic Relational Models, System Quality Analysis, Enterprise Architecture, Performance assessment, Probabilistic Relational Models
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-80630DOI: 10.1109/EDOC.2010.29Scopus ID: 2-s2.0-79951934826ISBN: 978-076954163-1 (print)OAI: oai:DiVA.org:kth-80630DiVA: diva2:496561
Conference
2010 14th IEEE International Enterprise Distributed Object Computing Conference, EDOC 2010; Vitoria; 25 October 2010 through 29 October 2010
Note
QC 20120222Available from: 2012-02-10 Created: 2012-02-10 Last updated: 2012-02-22Bibliographically approved

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Johnson, Pontus

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