A metamodel and taxonomy to facilitate context-aware service adaptation. (May 2015)

• Record Type: Journal Article
• Authors: Peinado, Sonia; Ortiz, Guadalupe; Dodero, Juan Manuel
• Is Part Of: Computers & electrical engineering. Volume 44(2015)
• Page Start: 262
• Page End: 279
• Publication Date: 2015-05
• Subjects: Metamodel -- Context-awareness -- Web services -- Model-driven development; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.02.004
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract : Graphical abstract: Highlights: A context metamodel to classify different types of contexts has been provided. MoDAS was improved through an XML document that facilitates context code-generation. MoDAS provides model-driven trustworthy and well-structured code. The aspect-oriented implementation facilitates system evolution and maintenance. Abstract: Increasingly, companies are adopting service-oriented architectures to respond to rapid changes in the market. Even though there are excellent tools and frameworks for service-oriented architecture adoption and service development, the latest adaptation to context has not been properly dealt with yet. Current approaches are mostly focused on solving context-aware issues for web applications only, focusing mainly on client-side adaptation, and there is a clear lack of context taxonomies which facilitate context-aware applications. In our previous work we proposed Model-Driven Adaptable Services (MoDAS): a methodology and tool for the development of context-aware services. In this paper, we propose two key improvements on MoDAS: firstly, leveraging the proposal's abstraction level, facilitating the use of a larger collection of contexts through the definition of an extensible context taxonomy by means of a metamodel; secondly, providing additional opportunities for code generation.