Abstract
Small- and medium-sized manufacturing companies must adapt their production processes more quickly. The speed with which enterprises can apply a change in the context of data integration and historicization affects their business. This article presents the possibilities of implementing the integration of control processes using modern technologies that will enable the adaptation of production lines. Integration using an object-oriented approach is suitable for complex tasks. Another approach is data integration using the entity referred to as tagging (TAG). Tagging is essential to apply for fast adaptation and modification of the production process. The advantage is identification, easier modification, and generation of data structures where basic entities include attributes, topics, personalization, locale, and APIs. This research proposes a model for integrating manufacturing enterprise data from heterogeneous levels of management. As a result, the model and the design procedure for data integrating production lines can efficiently adapt production changes.
Funder
Scientific Grant Agency of the Ministry of Education, Science, Research, and Sport of the Slovak Republic and the Slovak Academy of Sciences
European Regional Development Fund
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference48 articles.
1. TPC-DI: The first industry benchmark for data integration;Poess;Proc. VLDB Endow.,2014
2. Gölzer, P., Patrick, C., and Michael, A. (2022, September 19). Data Processing Requirements of Industry 4.0-Use Cases for Big Data Applications. Available online: https://aisel.aisnet.org/ecis2015_rip/61/.
3. IoT-based big data storage systems in cloud computing: Perspectives and challenges;Cai;IEEE Internet Things J.,2016
4. Automatic classification of data-warehouse-data for information lifecycle management using machine learning techniques;Nissen;Inf. Syst. Front.,2017
5. hCoCena: Horizontal integration and analysis of transcriptomics datasets;Oestreich;Bioinformatics,2022
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献