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Knowledge Object Modeling
Published in Denise Bedford, Knowledge Architectures, 2020
The earliest models focused on the modeling of systems such as databases. These early models focused on semantic or linguistic factors rather than entities (Chen, 1976). The development of object models and object modeling is relatively recent. The first entity-relationship model was developed in 1976 (P. Chen). These models were designed to overcome manual normalization challenges. Over the years, we have developed modeling methods for information objects (Integrated Definition Language 1 Extended (IDEF1X), the EXPRESS language, and the Unified Modeling Language (UML). Chen’s entity-relationship modeling methods were extended in the 1980s (C. Rolland) and formed an essential element of the information engineering movement of the 1990s (Lee, 1999; Martin et al., 1991). The shift to client-server architecture and the uncoupling of data and information from applications helped us to see the need to apply modeling to the objects themselves. Today, the modeling of information and data objects is a common practice. There is greater availability of and access to modeling methods and languages. Object models are no longer the purview of systems or information engineers but are generated by a wide range of practitioners and professionals.
Data-driven cloud simulation architecture for automated flexible production lines: application in real smart factories
Published in International Journal of Production Research, 2021
Dan Luo, Zailin Guan, Cong He, Yeming Gong, Lei Yue
Due to the increase in the diversity of products in actual manufacturing systems, production managers need different simulation models for different products. The construction of simulation models for large-scale production systems needs the requisite knowledge in professional fields, and consumes too much time. Therefore, to reduce the time of building simulation models, researchers have proposed a data-driven general simulation modelling method (Koyuncu et al. 2007; Celik et al. 2010; Wy et al. 2011; Celik and Son 2012; Liu et al. 2019). The core of this method is the construction of the universal simulation model and the interaction of the model with data (Colledani et al. 2013; Kádár, Terkaj, and Sacco 2013; Zhang et al. 2019). Several researchers have proposed different data formats for the data interaction between simulation software (Qiao 2003). For example, based on XML (Extensible Markup Language) and UML (Unified Modelling Language), Lee and Luo (2005) build a data transmission mechanism through DOM (Document Object Model) and XML path language, and achieve the conversion from workshop data to simulation model data. The data conversion comprises two steps (Fournier 2011; Gliwice 2012). In terms of general modelling technology, many scholars have realised the automated generation of simulation models based on various simulation software. Mackulak and Savory (2001) develop a generic module, which has been used for modelling a semiconductor material processing system (Lung et al. 1994), and the modelling cycle is reduced by 70%. Meng et al. (2013) propose a data-driven modelling and simulation framework for discrete event systems in the coal industry, proving that this framework can generate coal mine simulation models effectively and accurately to support various decisions. Jie (2009) uses data access technology to read the data required by the model to achieve the automated establishment process of the layout model. This method can improve the model reusability, but still requires the manual establishment of the verification model. Wang et al. (2009) propose to use IDEF0 (Integrated Computer Aided Manufacturing DEFinition method for Function Modelling) and IDEF1X (Integrated Computer Aided Manufacturing DEFinition method for Data Modelling) system to describe the system structure and function hierarchically, to establish a control model, and to increase the reusability of the underlying sub-models. Chen et al. (2014) propose more explicitly that we can consider system modelling at three levels, including the workshop, cell, and equipment level. The data-driven modelling and simulation technology is also one of the important technologies in the digital twin (Zhou, He, et al. 2019; Wang et al. 2020).