Automatized integration of a contextual model into a process with data variability. (December 2018)

• Record Type: Journal Article
• Authors: Simonin, Jacques; Puentes, John
• Is Part Of: Computer languages, systems & structures. Volume 54(2018)
• Page Start: 156
• Page End: 182
• Publication Date: 2018-12
• Subjects: Data variability -- Contextual data -- Model transformation -- Substitution transformation -- Enhancement transformation; Programming languages (Electronic computers) -- Periodicals; Computer networks -- Periodicals; Computer architecture -- Periodicals; Computer systems -- Periodicals; Langage de programmation; Réseau d'ordinateurs; Architecture d'ordinateur; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 005.13
• DOI: 10.1016/j.cl.2018.06.002
• Languages: English
• ISSNs: 1477-8424

Abstract

Highlights: A solution of context integration for a process with data production variability is proposed. The solution focuses on context integration by either substitution or enhancement. Both integrations are based on an extension of the model driven architecture approach. Substitution or enhancement rules conforming to this extended approach are specified. Modelling and rules coding of both integration cases show automatization feasibility. Abstract: Existent process models can hardly cope with the emerging issue of modelling exponential variable data volumes in systems' workflow, from specifications to operation. Given the strong relation between data context and data variability, this paper considers the automated integration of contextual models for processes with data variability. The proposed approach extends methodologically a platform independent model process, using a contextual data model, to obtain automatically the corresponding platform specific model. Contextual data are thus integrated to a process as a model, within a process. Two particular cases of contextual data models are studied in detail: substitution, when the contextual data model defines generated code, and enhancement, when learned data descriptions constitute the contextual data model. The feasibility and value of integrating a contextual model into a process to handle data variability are shown in detail describing these two use cases. Contextual model integration by substitution to include automatically variable ready to use application services to generate code, and contextual model integration by enhancement applied to supervised image classification based on variable descriptors. Results show that relating data variability and its context by means of automated integration of a designed system component model, simplifies variable data processing of system process models.