Citation: Deryabin, S.; Temkin, I.;
Rzazade, U.; Kondratev, E. Models
and Methods of Designing
Data-Centric Microservice
Architectures of Digital Enterprises.
Informatics 2023, 10, 4. https://
doi.org/10.3390/informatics10010004
Academic Editors: Sanjay Misra,
Robertas Damaševiˇcius and
Bharti Suri
Received: 30 November 2022
Revised: 29 December 2022
Accepted: 3 January 2023
Published: 5 January 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Models and Methods of Designing Data-Centric Microservice
Architectures of Digital Enterprises
Sergey Deryabin * , Igor Temkin, Ulvi Rzazade and Egor Kondratev
Institute of Information Technology and Computer Science, Department of Automated Control Systems,
National University of Science and Technology MISiS, Moscow 119049, Russia
* Correspondence: deryabin.sa@misis.ru
Abstract:
The article is devoted to methods and models of designing systems for the digital transfor-
mation of industrial enterprises within the framework of the Industry 4.0 concept. The purpose of this
work is to formalize a new notation for graphical modeling of the architecture of complex large-scale
systems with data-centric microservice architectures and to present a variant of the reference model
of such an architecture for creating an autonomously functioning industrial enterprise. The paper
provides a list and justification for the use of functional components of a data-centric microservice
architecture based on the analysis of modern approaches to building systems and the authors’ own
results obtained during the implementation of a number of projects. The problems of using traditional
graphical modeling notations to represent a data-centric microservice architecture are considered.
Examples of designing a model of such an architecture for a mining enterprise are given.
Keywords:
DEAL 1.0; DEA 1.0; digital enterprise architecture; data-centric microservices architecture;
digital transformation of enterprises; Industry 4.0; graphical modeling language; software design;
autonomous production
1. Introduction
Today, one of the main trends in the development of technological production is the
digital transformation of enterprises within the framework of the Industry 4.0 concept [
1
–
6
]
(Figure 1). The term digital transformation is understood as a transition to a qualitatively
new level of business process implementation, which implies minimizing or completely
excluding (where possible) human participation in technological work [
1
,
2
,
7
–
9
]. It means
that currently implemented business processes with direct human participation, including
with the help of information and automated systems, should be reduced to an autonomous
executable software and hardware form. Thus, the key vector of digital transformation of
industrial enterprises is the development of new software and hardware intelligent systems
based on modern and promising technologies and tools of Industry 4.0 [1–11].
To one degree or another, most industrial enterprises are now actively working in
the field of development, implementation, and pilot testing of robotic complexes with
various levels of autonomy, including unmanned aerial vehicles, unmanned transport
systems and technological installations, as well as the use of certain artificial intelligence
methods for solving problems of monitoring, planning, and managing technological pro-
cesses
[6,8,9,12,13]
. A relatively new direction can be attributed to the concept of a Digital
Twin of an enterprise, which is a high-precision dynamic virtual representation of the
enterprise, which has a two-way control connection with its physical counterpart and
represents the de facto finalizing part of the digital transformation [2,11–23].
At the same time, despite success in the practical implementation of individual tech-
nologies, the issues of organizing effective integration of all solutions in the form of a com-
plete autonomous production system remain obvious and critical problems on the way to
their scaling and, in fact, to the actual digital transformation of enterprises
[1,3–5,8,14,15,17]
.
Informatics 2023, 10, 4. https://doi.org/10.3390/informatics10010004 https://www.mdpi.com/journal/informatics
