Conceptual Model Interoperability: A Metamodel-driven Approach

Fillottrani, Pablo Rubén; Keet, C. Maria

doi:10.1007/978-3-319-09870-8_4

Cited by 11 publications

(3 citation statements)

References 16 publications

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“…These insights formed the basis of a metamodel-driven approach to interoperability of conceptual data models (Fillottrani and Keet, 2014). Instead of requiring a mesh network of mappings, each model maps into the metamodel or out of the metamodel, therewith reducing the number of mappings.…”

Section: Contextmentioning

confidence: 99%

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A framework for interoperability between models with hybrid tools

2022

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Complex system development and maintenance face the challenge of dealing with different types of models due to language affordances, preferences, sizes, and so forth that involve interaction between users with different levels of proficiency. Current conceptual data modelling tools do not fully support these modes of working. It requires that the interaction between multiple models in multiple languages is clearly specified to ensure they keep their intended semantics, which is lacking in extant tools. The key objective is to devise a mechanism to support semantic interoperability in hybrid tools for multi-modal modelling in a plurality of paradigms, all within one system. We propose FaCIL, a framework for such hybrid modelling tools. We design and realise the framework FaCIL, which maps UML, ER and ORM2 into a common metamodel with rules that provide the central point for management among the models and that links to the formalisation and logic-based automated reasoning. FaCIL supports the ability to represent models in different formats while preserving their semantics, and several editing workflows are supported within the framework. It has a clear separation of concerns for typical conceptual modelling activities in an interoperable and extensible way. FaCIL structures and facilitates the interaction between visual and textual conceptual models, their formal specifications, and abstractions as well as tracking and propagating updates across all the representations. FaCIL is compared against the requirements, implemented in crowd 2.0, and assessed with a use case. The proof-of-concept implementation in the web-based modelling tool crowd 2.0 demonstrates its viability. The framework also meets the requirements and fully supports the use case.

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Section: Contextmentioning

confidence: 99%

“…ER-1O and ER-1S take the KF primitives generated in the previous step and generate the corresponding ER primitives: Entity types and Subtypes. Further conversion must be applied to UML Attributes and Associations Constraints (cardinalities/ disjointness) (Fillottrani and Keet 2014).…”

Section: Contextmentioning

confidence: 99%

A framework for interoperability between models with hybrid tools

2022

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“…1. If C is not in EER, then convert it into EER, using a mapping or metamodel-driven approach; see, e.g., [31,32] and references therein. 2.…”

Section: Flexibility In the Knowid Inputmentioning

confidence: 99%

KnowID: An Architecture for Efficient Knowledge-Driven Information and Data Access

Fillottrani

Keet

2020

Data Intelligence

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Modern information systems require the orchestration of ontologies, conceptual data modeling techniques, and efficient data management so as to provide a means for better informed decision-making and to keep up with new requirements in organizational needs. A major question in delivering such systems, is which components to design and put together to make up the required “knowledge to data” pipeline, as each component and process has trade-offs. In this paper, we introduce a new knowledge-to-data architecture, KnowID. It pulls together both recently proposed components and we add novel transformation rules between Enhanced Entity-Relationship (EER) and the Abstract Relational Model to complete the pipeline. KnowID's main distinctive architectural features, compared to other ontology-based data access approaches, are that runtime use can avail of the closed world assumption commonly used in information systems and of full SQL augmented with path queries.

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Evidence-Based Languages for Conceptual Data Modelling Profiles

Fillottrani

Keet

2015

Advances in Databases and Information Systems

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Multiple logic-based reconstruction of conceptual data modelling languages such as EER, UML Class Diagrams, and ORM exists. They mainly cover various fragments of the languages and none are formalised such that the logic applies simultaneously for all three modelling language families as unifying mechanism. This hampers interchangeability, interoperability, and tooling support. In addition, due to the lack of a systematic design process of the logic used for the formalisation, hidden choices permeate the formalisations that have rendered them incompatible.We aim to address these problems, first, by structuring the logic design process in a methodological way. We generalise and extend the DSL design process to apply to logic language design more generally and, in particular, by incorporating an ontological analysis of language features in the process. Second, availing of this extended process, of evidence gathered of language feature usage, and of computational complexity insights from Description Logics (DL), we specify logic profiles taking into account the ontological commitments embedded in the languages. The profiles characterise the minimum logic structure needed to handle the semantics of conceptual models, enabling the development of interoperability tools. There is no known DL language that matches exactly the features of those profiles and the common core is small (in the tractable ALN I). Although hardly any inconsistencies can be derived with the profiles, it is promising for scalable runtime use of conceptual data models.

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Conceptual Model Interoperability: A Metamodel-driven Approach

Cited by 11 publications

References 16 publications

A framework for interoperability between models with hybrid tools

A framework for interoperability between models with hybrid tools

KnowID: An Architecture for Efficient Knowledge-Driven Information and Data Access

Evidence-Based Languages for Conceptual Data Modelling Profiles

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