|
ИСТИНА |
Войти в систему Регистрация |
ИПМех РАН |
||
Management of Complex Product Data Using Incremental Semantic Validationстатья
Информация о цитировании статьи получена из
Scopus
Дата последнего поиска статьи во внешних источниках: 16 января 2019 г.
- Авторы: Denis Ilyin, Sergey Morozov, Vitaly Semenov, Oleg Tarlapan
- Сборник: Transdisciplinary Engineering Methods for Social Innovation of Industry 4.0
- Серия: Advances in Transdisciplinary Engineering
- Том: 7
- Год издания: 2018
- Издательство: IOS PRESS
- Местоположение издательства: NIEUWE HEMWEG 6B, AMSTERDAM, NETHERLANDS, 1013 BG
- Первая страница: 956
- Последняя страница: 965
- DOI: 10.3233/978-1-61499-898-3-956
- Аннотация: Management of semantically complex product data is one of the challenging problems tightly connected with emerging concurrent engineering environments and next generation product data management systems (PDM). Although ACID principles (Atomicity, Consistency, Isolation, and Durability) are widely recognized and recommended for any information system, it is hard to guarantee the consistency of the product data. Such data are usually driven by formal models in EXPRESS language being part of the STEP standard (ISO 10303). To be consistent and unambiguously interpretable by computer programs the data must satisfy syntactic and semantic rules defined by the standards. Available PDM systems are rather limited in maintaining the data consistency. Complete semantic validation requires extremely high costs, often exceeding the processing time of individual transactions. Periodic validation or validation on user demand is possible, but at a high risk of losing data that become useless in case of rule violation. In the paper an effective incremental method for semantic validation of product data is presented. It is guaranteed that the final data revision is consistent if only the original revision was consistent and the spot rules were not violated. Static analysis of the model specifications is applied and a dependency graph is formed. The dependency graph enables to identify the spot rules and the data that should be inspected against the rules. Computational experiments prove the effectiveness of the method in conformity to complex large-scale product data managed under an innovative platform PDMhub.
- Добавил в систему: Морозов Сергей Вячеславович