RESTful Web Services are gaining increasing attention from both the service and the Web communities. The rising number of services being implemented and made available on the Web is creating a demand for modelling techniques that can abstract REST design from the implementation in order better to specify, analyse and implement large-scale RESTful Web systems. It can also help by providing suitable RESTful Web Service composition methods which can reduce costs by effi ciently re-using the large number of services that are already available and by exploiting existing services for complex business purposes. This research considers RESTful Web Services as state transition systems and proposes a novel Linear Logic based approach, the first of its kind, for both the modelling and the composition of RESTful Web Services. The thesis demonstrates the capabilities of resource-sensitive Linear Logic for modelling five key REST constraints and proposes a two-stage approach to service composition involving Linear Logic theorem proving and proof-as-process based on the π-calculus. Whereas previous approaches have focused on each aspect of the composition of RESTful Web Services individually (e.g. execution or high-level modelling), this work bridges the gap between abstract formal modelling and application-level execution in an efficient and effective way. The approach not only ensures the completeness and correctness of the resulting composed services but also produces their process models naturally, providing the possibility to translate them into executable business languages. Furthermore, the research encodes the proposed modelling and composition method into the Coq proof assistant, which enables both the Linear Logic theorem proving and the π-calculus extraction to be conducted semi-automatically. The feasibility and versatility studies performed in two disparate user scenarios (shopping and biomedical service composition) show that the proposed method provides a good level of scalability when the numbers of services and resources grow.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:577765 |
| Date | January 2013 |
| Creators | Zhao, Xia |
| Publisher | University of Bedfordshire |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/10547/301103 |
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