When metamodels evolve, model conformity may be broken. This forces the owners of the models (modelers) to intervene because it is impossible to automatically discover what to change in order to regain conformity. This paper presents ASIMOV, a platform for model and metamodel coevolution based on two hypothesis: i) a metamodeler knows the rationale behind metamodel changes, and is capable of providing guidelines for model coevolution; ii) the modeler is the only one in grade of making final decisions about his models. ASIMOV provides two languages for metamodelers: ASIMOV Evolution, to specify changes in the metamodels; and ASIMOV Assistance, to propose corresponding changes in the models. Also, ASIMOV Engine solves automatically the changes in models that can be automatically solved and assists modelers in coevolving their models to regain conformity. Moreover, modelers can adapt the proposed changes to suit their particular needs, introducing additional information when it is required. ASIMOV is here illustrated in the context of Enterprise Architecture projects.
The linguistic conformance and the ontological conformance between models and metamodels are two different aspects that are frequently mixed. This specifically occurs in the EMF framework resulting in problems such as the incapability to load and modify metamodels at runtime. In this paper we present a strategy to solve this problem by separating the ontological and the linguistic aspects of a metamodel and a metamodeling framework. The strategy has been implemented in a graphical editor and is motivated in the context of Enterprise Architecture Projects.
The business is an abstraction of the way in which value is created and delivered. The concrete representation is the business model, expressed by a group of artifacts built with different languages. It serves to describe, explain, analyze, design, and evaluate the business. The set of concepts, construction rules, artifacts, and languages required to express it, are defined by a Meta-Business Model (MBM). Multiple authors have proposed different MBMs, each one with a specific motivation and objective. Some of these MBMs are widely recognized and have been applied in contexts like innovation and entrepreneurship. Due to new challenges, such as sustainability, being faced by businesses and given new ways of producing and delivering value, like the sharing economy, Novel Complex Businesses (NCBs) are emerging. NCBs are businesses characterized by circular structures made out of numerous inter-related components, and by creating value out of the product/service schema. While existing MBMs fulfill certain purposes, they do not have the expressiveness required to describe NCBs precisely enough to describe and analyze them. This paper introduces an MBM with the concepts, construction rules, and graphical notation needed to represent NCBs. We also illustrate an NCB and present the results of the validation for our MBM.
After several years of teaching programming using an active learning approach, we present our Interactive Learning Objects (ILOs) as one of the components that reinforce our pedagogical model, by supporting the generation of high-level programming skills. In this paper, we suggest a multi-dimension taxonomy for ILOs and present the experimentation developed to evaluate the impact of these objects within our CS courses.
Since many workflow applications are used in contexts where the requirements and business rules change frequently, it is necessary to build those applications using strategies and tools that favor adaptation and reuse. The goal of this paper is to show an approach to build these extensible workflow applications using synchronized executable models. This approach uses concepts related to aspect-oriented software development, such as concern separation and instrumentation; thus, in addition to presenting the approach, we discuss our view on the central characteristics that define aspectmodeling, and we show how these concepts relate to our work and how they can be applied to workflow applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.