ML2: an expressive multi-level conceptual modeling language

Detalhes bibliográficos
Ano de defesa: 2017
Autor(a) principal: Fonseca, Claudenir Morais
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: eng
Instituição de defesa: Universidade Federal do Espírito Santo
BR
Mestrado em Informática
Centro Tecnológico
UFES
Programa de Pós-Graduação em Informática
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
004
Link de acesso: http://repositorio.ufes.br/handle/10/9851
Resumo: Subject domains are often conceptualized with entities stratified into a rigid two-level structure: a level of classes and a level of individuals which instantiate these classes. Multi-level modeling extends the conventional two-level classification scheme by admitting classes that are also instances of other classes, a feature which can be used beneficially in a number of domains. Despite the advances in multi-level modeling in the last decade, a number of requirements arising from representation needs in subject domains with multiple levels of classification have not yet been addressed in current modeling approaches. In this work, we investigate the requirements for multi-level modeling and propose an expressive multi-level conceptual modeling language dubbed ML2. We follow here a systematic approach based on a strict separation of concerns. First, we capture and formalize the conceptualization underlying multilevel modeling phenomena, called MLT*, building on the multi-level theory called MLT. Second, we employ MLT* as bedrock for the definition of ML2, a textual modeling language that addresses the elicited requirements for multi-level modeling. The proposed language is supported by a featured Eclipse-based workbench which verifies adherence of the ML2 model to the MLT* rules. The capabilities of ML2 are demonstrated by using it to accomplish three distinct modeling tasks: modeling a multi-level challenge proposed in the context of the MULTI 2017 workshop; modeling the concepts from ML2‟s underlying theory, MLT*; modeling the Unified Foundation Ontology (UFO).