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From BPEL to SRML: A Formal Transformational Approach

Bocchi, Laura, Hong, Yi, Lopes, Antónia, Fiadeiro, José Luiz (2008) From BPEL to SRML: A Formal Transformational Approach. In: Web Services and Formal Methods. 4th International Workshop, WS-FM 2007 Proceedings. Lecture Notes in Computer Science . pp. 92-107. Springer, Berlin, Germany ISBN 978-3-540-79229-1. E-ISBN 978-3-540-79230-7. (doi:10.1007/978-3-540-79230-7_7) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:59185)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1007/978-3-540-79230-7_7

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.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1007/978-3-540-79230-7_7
Uncontrolled keywords: model transformation; transformation rule; graph transformation; composite service; model business process
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Laura Bocchi
Date Deposited: 28 Nov 2016 10:42 UTC
Last Modified: 16 Nov 2021 10:23 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/59185 (The current URI for this page, for reference purposes)

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