From BPEL to SRML: A formal transformational approach

Laura Bocchi, Yi Hong, Antónia Lopes, José Luiz Fiadeiro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

The SENSORIA Reference Modelling Language (SRML) provides primitives for modelling business processes in a technology agnostic way. At the core of SRML is the notion of module as a composition of tightly coupled components and loosely coupled, dynamically discovered services. This paper presents an encoding of BPEL processes into SRML modules using model transformation techniques. The encoding provides the means to create high-level declarative descriptions of BPEL processes that can be used for building more complex modules, possibly including components implemented in other languages. The composition can be modelled and analysed as an ensemble, relying on the rich formal framework that is being developed within SENSORIA.

Original languageEnglish
Title of host publicationWeb Services and Formal Methods - 4th International Workshop, WS-FM 2007, Proceedings
Pages92-107
Number of pages16
Volume4937
DOIs
Publication statusPublished - 14 May 2008
Event4th International Workshop on Web Services and Formal Methods, WS-FM 2007 - Brisbane, Australia
Duration: 28 Sep 200729 Sep 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4937 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International Workshop on Web Services and Formal Methods, WS-FM 2007
CountryAustralia
CityBrisbane
Period28/09/0729/09/07

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  • Cite this

    Bocchi, L., Hong, Y., Lopes, A., & Fiadeiro, J. L. (2008). From BPEL to SRML: A formal transformational approach. In Web Services and Formal Methods - 4th International Workshop, WS-FM 2007, Proceedings (Vol. 4937, pp. 92-107). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4937 LNCS). https://doi.org/10.1007/978-3-540-79230-7_7