Metadata are like the steam engine of the 21st century, driving businesses and offer multiple enhancements. Nevertheless, many companies are unaware that these data can be used efficiently to improve their own operation. This is where the Enterprise Architecture Framework comes in. It empowers an organisation to get a clear view of their business, application, technical and physical layer. This modelling approach is an established method for organizations to take a deeper look into their structure and processes. The development of such models requires a great deal of effort, is carried out manually by interviewing stakeholders and requires continuous maintenance. Our new approach enables the automated mining of Enterprise Architecture models. The system uses common technologies to collect the metadata based on network traffic, log files and other information in an organisation. Based on this, the new approach generates EA models with the desired views points. Furthermore, a rule and knowledge-based reasoning is used to obtain a holistic overview. This offers a strategic decision support from business structure over process design up to planning the appropriate support technology. Therefore, it forms the base for organisations to act in an agile way. The modelling can be performed in different modelling languages, including ArchiMate and the Nato Architecture Framework (NAF). The designed approach is already evaluated on a small company with multiple services and an infrastructure with several nodes.
翻译:元数据类似于21世纪的蒸汽引擎,驱动企业并提供多重增强。然而,许多公司不知道这些数据能够被高效地用于改善自己的运作。这是企业架构框架的源头。它授权一个组织对其业务、应用、技术和物理层有清晰的了解。这种建模方法是各组织更深入地审视其结构和过程的既定方法。这种模型的开发需要大量努力,通过访谈利益攸关方进行手工操作,需要持续维护。我们的新办法使得企业架构模型的自动挖掘得以进行。该系统使用共同技术收集基于网络流量、日志文档和其他信息的元数据。基于这个方法,新办法生成了带有理想观点的EA模型。此外,还使用规则和基于知识的推理来获得一个全面的概览。它从业务结构到流程设计的战略决策支持,以规划适当的支持技术。因此,它构成了各组织以灵活方式采取行动的基础。建模可以使用不同的模拟语言进行,包括ArchiMate和Nato建筑框架(NAF)进行。设计的方法已经用多种基础设施对公司进行了评估。