Toward an Ontology-Driven Unifying Metamodel for UML Class Diagrams, EER, and ORM2

Keet, C. Maria; Fillottrani, Pablo Rubén

doi:10.1007/978-3-642-41924-9_26

Cited by 8 publications

(14 citation statements)

References 30 publications

(40 reference statements)

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“…The metamodel, described in [16,17] and represented as a set of UML v.2.4 diagrams with annotations, is a consistent conceptual model about the entities and constraints in the selected modelling languages, covering almost all their native features. It aims at representing in a unified way whatever is present in the languages, and several notions from Ontology (philosophy) and ontologies (Artificial intelligence) were used in its development so as to increase understanding of the language features, to reconcile or unify perceived differences, and to improve the quality of the metamodel; e.g., on attributes and the positionalist nature of relationships [17].…”

Section: Formalised Metamodel and Term Mappingsmentioning

confidence: 99%

“…It aims at representing in a unified way whatever is present in the languages, and several notions from Ontology (philosophy) and ontologies (Artificial intelligence) were used in its development so as to increase understanding of the language features, to reconcile or unify perceived differences, and to improve the quality of the metamodel; e.g., on attributes and the positionalist nature of relationships [17]. It is formalized in two versions, both available at http://www.meteck.org/ SAAR.html.…”

Section: Formalised Metamodel and Term Mappingsmentioning

confidence: 99%

“…The metamodel is complemented with a vocabulary in the form of a list of terminology comparison and conventions of the entities in UML Class Diagrams, EER, and ORM, and their corresponding names in the metamodel (see [17]). …”

Section: Formalised Metamodel and Term Mappingsmentioning

confidence: 99%

“…The four lists largely follow from the metamodel of the static, structural components and constraints [17], although in some cases there is a conceptual equivalence, but not exactly in the representation; e.g., UML and EER both have attributes, but EER does not record the datatype, so they are not 1:1 from an algorithmic viewpoint. Transformations.…”

Section: Categorisation Of Rulesmentioning

confidence: 99%

“…The previously developed metamodel [16,17] with all static structural entities (including constraints) of the main conceptual modelling languages-UML v2.4.1, EER, and ORM2-has been formalised and has a table with mappings between terms used in the different languages (e.g., UML association and EER relationship). This is used with a newly specified set of rules from/to the metamodel and within-metamodel conversions to convert model elements and check the validity of inter-model assertions.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Conceptual Model Interoperability: A Metamodel-driven Approach

Fillottrani

Keet

2014

Rules on the Web. From Theory to Applications

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Abstract. Linking, integrating, or converting conceptual data models represented in different modelling languages is a common aspect in the design and maintenance of complex information systems. While such languages seem similar, they are known to be distinct and no unifying framework exists that respects all of their language features in either model transformations or inter-model assertions to relate them. We aim to address this issue using an approach where the rules are enhanced with a logic-based metamodel. We present the main approach and some essential metamodel-driven rules for the static, structural, components of ER, EER, UML v2.4.1, ORM, and ORM2. The transformations for model elements and patterns are used with the metamodel to verify correctness of inter-model assertions across models in different languages.

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Section: Formalised Metamodel and Term Mappingsmentioning

confidence: 99%

Section: Formalised Metamodel and Term Mappingsmentioning

confidence: 99%

Section: Formalised Metamodel and Term Mappingsmentioning

confidence: 99%

Section: Categorisation Of Rulesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Conceptual Model Interoperability: A Metamodel-driven Approach

Fillottrani

Keet

2014

Rules on the Web. From Theory to Applications

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A general framework for big data knowledge discovery and integration

Wang

Lin

et al. 2018

Concurrency and Computation

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Data structure description, conceptual modeling, and logic reasoning for knowledge discovery are three critical factors for the integration of information with heterogeneity. In particular, technologies of NoSQL databases and Internet of Things raise an urgent requirement for a uniform expression of heterogeneous data, and little attention has been paid to researches on the integration of NoSQL databases with traditional data models, as well as the semantic description of big data. To tackle these problems, in this paper, a concept-and-relation-oriented grid data model called GODM model is first proposed based on the definitions of Monad, Compounder, Relation, etc. Then, the GODM model is utilized to uniformly describe traditional data models and NoSQL data models, which eliminates structure differences of heterogeneous data. Next, based on the GODM relation mechanism, an extendable semantic system is built up by choosing SHOIQ(D) description logic as the example to establish the correspondence with GODM grammar subset, providing a fundamental support for semantic integration and knowledge discovery of heterogeneous data. After that, comprehensive comparisons with GODM and other models are made, especially the distinctions between GODM and OWL on the aspects of relation mechanism, hybrid schema, description logic, grammatical constructors, etc. Besides, experimental evaluations and analyses on time and space efficiencies of some primary common data models are conducted after the proposal of a general evaluation model, with the results showing that the GODM model has great advantage on properties of expressiveness, flexibility, etc, particularly time and space efficiency. In summary, the GODM model describes heterogeneous data from both aspects of data structure and semantic relationship and realizes a hybrid schema reconciling the schemaful and schemaless data models, making it especially suitable for dynamic data integration and knowledge discovery from big data models. KEYWORDSdata integration, data model, GODM, hybrid schema, knowledge representation, NoSQL, time and space efficiency INTRODUCTIONThe development of cloud computing and big data technologies prompts data to expand dramatically and uncontrollably in size, structure, and format. To eliminate data heterogeneity, a unified mechanism is necessary to provide consistent data manipulation approaches and tap the value of mass data in maximum. According to the implementation technique and application requirement, data integration approaches are generally classified into a materialized approach and a virtual approach, 1 where the materialized approaches mainly refer to data warehousing, 2 and the virtual approaches have two sub-branches: structural approaches and semantic approaches. 1 The warehousing methods preprocess data before integration, and all the integrated data are loaded in the warehouse to guarantee the efficiency of a data query. However, this kind of methods always lead Concurrency Computat Pract Exper. 2018;30:e4422. wileyonlinelibrary.com/j...

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