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Принципы полной и корректной трансформации синхронизируемых моделей

Принципы полной и корректной трансформации синхронизируемых моделей

Abstract

The article describes the method of complete and correct model transformation within a model synchronization scenario. It is based on a strategy of producing a complete and correct set of transformation rules, ensuring the completeness and correctness criteria by analyzing a syntactic and semantic structure of meta-models via graph grammar techniques. Currently used approaches to creating the model transformation rules are considered. Their disadvantages which influence the result of the model transformation process are demonstrated. Correlations between the correctness of the meta-models and transformation rules as well as the completeness and correctness of the transformation process are found. The proposed method allows both creating a correct set of the model transformation rules automatically and determining a sequence of this creation. The evidences of this fundamental result are provided

Предложена методика полной и корректной трансформации моделей для решения задачи их синхронизации. В ее основе лежит стратегия построения полного и корректного набора правил трансформации, обеспечивающих выполнение критериев корректности и полноты путем анализа синтаксической и семантической структуры метамоделей с использованием аппарата графовых грамматик. Рассмотрены применяемые в настоящее время подходы для создания правил трансформации моделей, показаны их недостатки, влияющие на результат трансформации, выявлена связь между корректностью метамоделей правилами трансформации, а также полнотой и корректностью процесса трансформации. Предлагаемая методика не только позволяет автоматически создавать указанный набор правил трансформации моделей, но также определять порядок порождения правил. Приведены принципы доказательства этого основополагающего результата

Keywords

ТРАНСФОРМАЦИЯ МОДЕЛЕЙ, СИНХРОНИЗАЦИЯ МОДЕЛЕЙ, ГРАФОВЫЕ ГРАММАТИКИ, ПОЛНОТА И КОРРЕКТНОСТЬ ТРАНСФОРМАЦИИ МОДЕЛЕЙ

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
bronze