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      Enterprise Integration and Interoperability for Big Data-Driven Processes in the Frame of Industry 4.0

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          Abstract

          Traditional manufacturing businesses lack the standards, skills, processes, and technologies to meet today's challenges of Industry 4.0 driven by an interconnected world. Enterprise Integration and Interoperability can ensure efficient communication among various services driven by big data. However, the data management challenges affect not only the technical implementation of software solutions but the function of the whole organization. In this paper, we bring together Enterprise Integration and Interoperability, Big Data Processing, and Industry 4.0 in order to identify synergies that have the potential to enable the so-called “Fourth Industrial Revolution.” On this basis, we propose an architectural framework for designing and modeling Industry 4.0 solutions for big data-driven manufacturing operations. We demonstrate the applicability of the proposed framework through its instantiation to predictive maintenance, a manufacturing function that increasingly concerns manufacturers due to the high costs, safety issues, and complexity of its application.

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          A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems

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            Big Data: A Survey

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              Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems

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                Author and article information

                Contributors
                Journal
                Front Big Data
                Front Big Data
                Front. Big Data
                Frontiers in Big Data
                Frontiers Media S.A.
                2624-909X
                03 June 2021
                2021
                : 4
                : 644651
                Affiliations
                Information Management Unit (IMU), School of Electrical and Computer Engineering, National Technical University of Athens (NTUA) , Athens, Greece
                Author notes

                Edited by: Federica Mandreoli, University of Modena and Reggio Emilia, Italy

                Reviewed by: Philipp Wieder, Gesellschaft für Wissenschaftliche Datenverarbeitung (MPG), Germany; Guanfeng Liu, Macquarie University, Australia

                *Correspondence: Alexandros Bousdekis albous@ 123456mail.ntua.gr

                This article was submitted to Data Mining and Management, a section of the journal Frontiers in Big Data

                Article
                10.3389/fdata.2021.644651
                8210777
                d066b4a7-efb2-4624-8732-261d7577b01f
                Copyright © 2021 Bousdekis and Mentzas.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 21 December 2020
                : 13 April 2021
                Page count
                Figures: 7, Tables: 8, Equations: 0, References: 109, Pages: 18, Words: 12246
                Categories
                Big Data
                Original Research

                conceptual modeling,data analytics,enterprise architecture,data management,smart manufacturing,predictive maintenance

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