An ontology-driven unifying metamodel of UML Class Diagrams,EER, and ORM2
AsbtractSoftware interoperability and application integration can be realized through using theirrespective conceptual data models, which may be represented in...
Methodological guidelines for reusing general ontologies
Currently, there is a great deal of well-founded explicit knowledge formalizing general notions,such as time concepts and thepart_ofrelation. Yet,...
A Unified Modelling Language without referential redundancy
The paper argues that, as a language for representing concrete problem domains, the quality of the UMLis compromised by...
Variable set semantics for keyed generalized sketches:formal semantics for object identity and abstract syntaxfor...
We introduce a mathematical framework where a formal semantics for object identity can be built ir-respectively to computer related...
Towards an accurate functional size measurement procedurefor conceptual models in an MDA environment
The accurate measurement of the functional size of applications that are automaticallygenerated in MDA environments is a challenge for...
Evaluating quality of conceptual modelling scripts basedon user perceptions
This paper presents the development of a user evaluations based quality model for conceptual modelling scripts applyingSeddon’s variant of...
Theoretical and practical issues in evaluating the qualityof conceptual models: current state and future...
An international standard has now been established for evaluating the quality of software products.However there is no equivalent standard...
Multi-level ontology-based conceptual modeling
Since the late 1980s, there has been a growing interest in the use of foundational ontologies toprovide a sound...
Big data technologies and Management: What conceptualmodeling can do
The era of big data has resulted in the development and applications of technologies andmethods aimed at effectively using...
Business-driven data analytics: A conceptual modeling framework
The effective development of advanced data analytics solutions requires tackling challenges suchas eliciting analytical requirements, designing the machine learning...
