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Enterprise Modeling: From Digital Transformation to Digital Ubiquity

Fill Hans-Georg

Annals of Computer Science and Information Systems

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2020

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Vol. 21

1--4

EN

While digital transformation is still a challenge for many companies when introducting digital technologies in existing processes and business models, digital ubiquity stands for the next step in digitalization. It characterizes the omnipresence of a large range of digital technologies, connectivity, and data as well as entirely digital organizations. This includes for example upcoming technologies such as distributed ledgers, artificial intelligence or augmented reality and according interfaces and data sources as well as decentralized apps and autonomous organizations. The challenge thus becomes to optimally deal with these opportunities and deploy them efficiently in business scenarios. In this paper we will investigate the role of enterprise modeling under this paradigm and how it can contribute to a well-structured, systematic understanding of complex digital phenomena for supporting business and technological decisions.

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Identification methods based on associative search procedure

Bakhtadze N. N., Kulba V. V., Yadikin I. B., Lototsky V. A., Maximov E. M.

Management and Production Engineering Review

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2011

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Vol. 2, No. 3

6-18

EN

In modern control systems, identification is an integral part of adaptive control where process models are adjusted using real-time operation data and control actions optimal with respect to some performance criterion are developed. A variety of identification methods based on mathematical statistics techniques have been developed. Algorithms optimal for certain classes of objects and external disturbances were categorized dependent on the available a priori information about the control object. The limits of approximating models development and application were outlined. Against this background, the paper presents novel associative search techniques enabling the development of a new dynamic object's model on each time step rather than plant approximation pertaining to time. The model is build using the data samples from process history (associations) developed at the learning phase. The new techniques employs the models of human individual's (process operator's, stock analyst's or trader's) behavior based on professional knowledge formalization. Application examples from oil refining and chemical industries, power engineering, and banking are adduced.

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An approach to coordination of power generating units

Bakhtadze N. N., Yadikin I. B., Kulba V. V., Lototsky V. A., Maximov E. M.

Control and Cybernetics

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2010

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Vol. 39, no 1

305-318

EN

The importance of the problem of power generating facility identification is justified against the background of interconnection of the European and Russian power grids. Intelligent control techniques for power generation facilities are presented. A methodology for estimating the dynamics of participation of the power grid generating facilities in the overall primary frequency regulation in contingency situations is developed based on frequency and generating capacity time series. Process identification algorithms, based on virtual model design using process data archives and knowledge bases, are discussed. Associative search methods are used for identification algorithm development.