P2AMF: Predictive, probabilistic architecture modeling frameworkShow others and affiliations
2013 (English)In: Lecture Notes in Business Information Processing, 2013, p. 104-117Conference paper, Published paper (Refereed)
Abstract [en]
In the design phase of business and software system development, it is desirable to predict the properties of the system-to-be. Existing prediction systems do, however, not allow the modeler to express uncertainty with respect to the design of the considered system. In this paper, we propose a formalism, the Predictive, Probabilistic Architecture Modeling Framework (P 2AMF), capable of advanced and probabilistically sound reasoning about architecture models given in the form of UML 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.
Place, publisher, year, edition, pages
2013. p. 104-117
Series
Lecture Notes in Business Information Processing, ISSN 1865-1348 ; 144
Keywords [en]
class diagram, Object Constraint Language, object diagram, prediction, probabilistic inference, system properties, UML, Class diagrams, Object diagrams, System property, Forecasting, Industry, Interoperability, Markup languages, Inference engines
National Category
Social Sciences
Identifiers
URN: urn:nbn:se:kth:diva-134654ISI: 000345294000010Scopus ID: 2-s2.0-84875673907ISBN: 9783642367953 (print)ISBN: 978-3-642-36796-0 (print)OAI: oai:DiVA.org:kth-134654DiVA, id: diva2:678126
Conference
5th International IFIP Working Conference on Enterprise Interoperability, IWEI 2013, 27 March 2013 through 28 March 2013, Enschede
Note
QC 20131211
2013-12-112013-11-272017-04-28Bibliographically approved