Evidence-based Languages for Conceptual Data Modelling Profiles

To improve database system quality as well as runtime use of conceptual models, many logic-based reconstructions of conceptual data modelling languages have been proposed in a myriad of logics. They each cover their features to a greater or lesser extent and are typically motivated from a logic viewpoint. This raises questions such as what would be an evidence-based common core and what is the optimal language profile for a conceptual modelling language family. Based on a common metamodel of UML Class Diagrams (v2.4.1), ER/EER, and ORM/2's static elements, a set of 101 conceptual models, and availing of computational complexity insights from Description Logics, we specify these profiles. There is no known DL language that matches exactly the features of those profiles and the common core is small (in the tractable $\mathcal{ALNI}$). Although hardly any inconsistencies can be derived with the profiles, it is promising for scalable runtime use of conceptual data models

Evidence-based lean conceptual data modelling languages

Multiple logic-based reconstructions of conceptual data modelling languages such as EER, UML Class Diagrams, and ORM exist. 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, we specify minimal logic profiles availing of this extended process, including the ontological commitments embedded in the languages, of evidence gathered of language feature usage, and of computational complexity insights from Description Logics (DL). The profiles characterise the essential logic structure needed to handle the semantics of conceptual models, therewith 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 DL ALNI). Although hardly any inconsistencies can be derived with the profiles, it is promising for scalable runtime use of conceptual data models

Lenguajes austeros de modelado conceptual de datos basados en evidencias

Multiple logic-based reconstructions of UML class diagram, Entity Relationship diagrams, and Obect-Role Model diagrams exists. They mainly cover various fragments of these Conceptual Data Modelling Languages and none are formalised such that the logic applies simultaneously for the three language families as a 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 logic language design. In particular, a new phase of ontological analysis of language features is included, to apply to logic language design more generally and, in particular, by incorporating an ontological analysis of language features in the process. Second, we specify minimal logic profiles availing of this extended process, including the ontological commitments embedded in the languages, of evidence gathered of language feature usage, and of computational complexity insights from Description Logics (DL). The profiles characterise the essential logic structure needed to handle the semantics of conceptual models, therewith enabling the development of interoperability tools. No known DL language matches exactly the features of those profiles and the common core is in the tractable DL ACJfl. Although hardly any inconsistencies can be derived with the profiles, it is promising for scalable runtime use of conceptual data models.Existen varias reconstrucciones basadas en lógica de lenguajes de modelado conceptual como EER, diagramas de clases UML y ORM. Principalmente cubren fragmentos de estos lenguajes, y sus formalizaciones no están hechas para que se apliquen simultáneamente a estas tres familias de lenguajes como un mecanismo de unificación. Este hecho atenta contra el intercambio y la interoperabilidad de los modelos y el desarrollo de herramientas de soporte. Además, dada la falta de un proceso sistemático de diseño, ciertas decisiones ocultas en la representación lógica hacen que las formalizaciones sean incompatibles. En este trabajo nos proponemos atacar este problema, proponiendo primero un proceso de diseño lógico que puede ser aplicado en forma metodológica. Se generaliza y extiende el proceso DSL para que se pueda aplicar al diseño de lenguajes lógicos en general, incorporando análisis ontológico de las características del lenguaje. Segundo, se especifican perfiles lógicos minimales que sacan provecho de este proceso extendido, incluyendo los compromisos ontológicos asumidos, de evidencia de uso de las características del lenguaje, y de los propiedades computacionales de las Lógicas Descriptivas (DL, description logics). Estos perfiles caracterizan la estructura lógica esencial que se necesita para manejar la semántica de los modelos conceptuales, habilitando el desarrollo de herramientas automáticas de interoperabilidad. No existe correspondencia exacta directa entre estos perfiles y fragmentos conocidos de lenguajes DL, y el núcleo común es pequeño (la lógica tratable ACNT). Aunque es muy poca la posibilidad de derivar inconsistencias dentro de estos perfiles, es prometedor su uso en modelos conceptuales dado su complejidad en tiempo escalable.Facultad de Informátic

Evidence-based lean logic profiles for conceptual data modelling languages

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 ALNI). Although hardly any inconsistencies can be derived with the profiles, it is promising for scalable runtime use of conceptual data models

Conceptual Model Interoperability: a Metamodel-driven Approach

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

A Framework for Interoperability Between Models with Hybrid Tools

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