2012
DOI: 10.1007/978-3-642-34002-4_39
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A Method for the Definition and Treatment of Conceptual Schema Quality Issues

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Cited by 16 publications
(31 citation statements)
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“…As an exception, the generated class is made concrete if it only has one subclass. This is in line with accepted conceptual schema quality issues [1].…”
Section: Synthesizing An Initial Meta-modelsupporting
confidence: 88%
See 1 more Smart Citation
“…As an exception, the generated class is made concrete if it only has one subclass. This is in line with accepted conceptual schema quality issues [1].…”
Section: Synthesizing An Initial Meta-modelsupporting
confidence: 88%
“…This transformation takes into account the recommended patterns, uses heuristics that follow accepted quality guidelines for conceptual schemas [1] and is configurable in several steps (in the implementation these options are selected through a wizard). The transformation proceeds as follows:…”
Section: Synthesizing An Initial Meta-modelmentioning
confidence: 99%
“…In [10], some design patterns for meta-models are proposed, while in [50], the requirements for meta-models are represented as use case diagrams and the metamodels are evolved by applying patterns. Related to our recommender system, in [2], the authors present a unifying approach to define quality issues on conceptual schemas. These issues include syntactical ones (like invalid cardinalities), naming conventions [1] (like upper camel case for the name of classes), best practices (like redundant generalizations) and basic quality issues (like concrete classes with abstract children).…”
Section: Related Workmentioning
confidence: 99%
“…From these fragments, a meta-model is automatically induced, which can be refactored if needed. The system provides suggestions of possible improvements based on well-known refactorings [30], quality issues for conceptual schemas [2], meta-model design patterns [10] and anti-patterns [29]. Once an initial version of the meta-model is obtained, it can be validated against new model examples, and the system reports any problematic model element as well as the missing meta-model elements needed to accept the model examples.…”
Section: Introductionmentioning
confidence: 99%
“…Refinement consists of providing language modifications by means of new examples, which are discussed by the end-users to reach agreement to drive the language development process. Furthermore, to help making design decisions, the approach also incorporates a recommender system which identifies and proposes changes in the language according to meta-model quality patterns [6][7][8].…”
Section: Introductionmentioning
confidence: 99%