Integrated Data and Knowledge Management as Key Factor for Industry 4.0

The Industry 4.0 paradigm has seen increased interest by scientific research. The emergence of new information and communication technologies (ICT) in manufacturing shop floors generates large quantities of data and information flows, leading to large databases and big data issues. To resolve these issues, there is a need of efficient data and knowledge management approaches to support the exploitation, sharing, and interpretation of useful data. This article discusses the topic of ICT implementation within an Industry 4.0 paradigm. This article identifies problems of digital chain disruption and the need for interoperability. A conceptual framework for the management and structuring of data and knowledge is proposed as a solution to address these concerns.

[1]  Alain Bernard,et al.  Facet-based approach for the management of information multi points-of- view in product modelling , 2017 .

[2]  Dewi Octaviani,et al.  RDB2Onto: An approach for creating semantic metadata from relational educational data , 2015, 2015 International Conference on Science in Information Technology (ICSITech).

[3]  Jorge Posada,et al.  9th International Conference on Knowledge Based and Intelligent Information and Engineering Systems a Perspective on Knowledge Based and Intelligent Systems Implementation in Industrie 4.0 , 2022 .

[4]  M. Sartor,et al.  Integration and Scale in the Context of Industry 4.0: The Evolving Shapes of Manufacturing Value Chains , 2019, IEEE Engineering Management Review.

[5]  Farouk Belkadi,et al.  Towards a knowledge-based framework for digital chain monitoring within the industry 4.0 paradigm , 2019, Procedia CIRP.

[6]  David Wang.  Building Value in a World of Technological Change: Data Analytics and Industry 4.0 , 2018, IEEE Engineering Management Review.

[7]  Jun Zhang,et al.  Industry 4.0 with cyber-physical integration: A design and manufacture perspective , 2015, 2015 21st International Conference on Automation and Computing (ICAC).

[8]  Michael Sony,et al.  Ten Lessons for Managers While Implementing Industry 4.0 , 2019, IEEE Engineering Management Review.

[9]  Kamran Munir,et al.  The use of ontologies for effective knowledge modelling and information retrieval , 2018, Applied Computing and Informatics.

[10]  Catherine Da Cunha,et al.  Knowledge-based multi-level aggregation for decision aid in the machining industry , 2019, ArXiv.

[11]  Mourad Messaadia,et al.  A meta-modelling framework for knowledge consistency in collaborative design , 2012, Annu. Rev. Control..

[12]  P. Dallasega.  Industry 4.0 Fostering Construction Supply Chain Management: Lessons Learned From Engineer-to-Order Suppliers , 2018, IEEE Engineering Management Review.

[13]  David Wang,et al.  An Enterprise Data Pathway to Industry 4.0 , 2018, IEEE Engineering Management Review.

[14]  V. R. Benjamins,et al.  Overview of Knowledge Sharing and Reuse Components: Ontologies and Problem-Solving Methods , 1999, IJCAI 1999.

[15]  D. Bishop.  Key Performance Indicators: Ideation to Creation , 2018, IEEE Engineering Management Review.

[16]  Thomas R. Gruber,et al.  Toward principles for the design of ontologies used for knowledge sharing? , 1995, Int. J. Hum. Comput. Stud..

[17]  Kwabena Agyapong-Kodua,et al.  Recipe-based Integrated Semantic Product, Process, Resource (PPR) Digital Modelling Methodology , 2014 .