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.

Abstract-A Schema-less NoSQL system refers to solutions where users do not declare a database schema and, in fact, its management is moved to the application code. This paper presents a study that allows us to evaluate, to some extent, the data structuring impact. The decision of how to structure data in semi-structured databases has an enormous impact on data size, query performance and readability of the code, which influences software debugging and maintainability. This paper presents an experiment performed using MongoDB along with several alternatives of data structuring and a set of queries having increasing complexity. This paper introduces an analysis regarding the findings of such an experiment.

In many domains, models are created based on predefined metamodels which abstract the structure of the domain in question. However, there are specific domains, like Enterprise Architecture (EA) projects, where a metamodel cannot be defined in advance to the creation of the model. Unfortunately, in this situation using standard frameworks, like EMF, generates some inconveniences in the construction of the model and the metamodel because these frameworks do not support the manipulation of metamodels at runtime. In this paper, we propose a strategy to co-create metamodels and models in an incremental and simultaneous way. This proposal is supported by a dynamic approach that separates the linguistic and the ontological conformity concerns of metamodeling. This strategy has been implemented in a graphical editor called GraCoT, which also provides interactive assistance to guide the users during the co-creation process.

Document-oriented bases allow high flexibility in data representation which facilitates a rapid development of applications and enables many possibilities for data structuring. Nevertheless, the structural choices remain crucial because of their impact on several aspects of the document base and application quality, e.g, memory print, data redundancy, readability and maintainability. Our research is motivated by quality issues of document-oriented bases. We aim at facilitating the study of the possibilities of data structuring and providing objective metrics to better reveal the advantages and disadvantages of each solution with respect to user needs. In this paper, we propose a set of structural metrics for a JSON compatible schema abstraction. These metrics reflect the complexity of the structure and are intended to be used in decision criteria for schema analysis and design process. This work capitalizes on experiences with MongoDB, XML and software complexity metrics. The paper presents the definition of the metrics together with a validation scenario where we discuss how to use the results in a schema recommendation perspective.