Identification of technologies in Industry 4.0 with the use of text mining

• Autorzy: Zdonek Dariusz

Identyfikatory

DOI

10.29119/1641-3466.2020.142.4

• Języki publikacji: EN

Abstrakty

EN

Purpose: The objective of this paper is to identify leading technologies in Industry 4.0. Design/methodology/approach: The identification was made with the use of text mining to explore the scientific texts in this field. Assumptions of own iterative method for analyzing scientific texts were proposed, with the use of R language, tokenization, lemmatization, n-grams and correspondence analysis. The assumptions of the proposed method were used to analyze the 40 most often quoted articles indexed in the Web of Science. Findings: On the basis of the obtained results, 4 leading technologies were identified. These are Cloud Computing, Internet of Things, Cyber-physical System and Big Data. Originality/value: The article proposes an original method of identifying the leading technologies used in Industry 4.0. The proposed method is based on text mining and correspondence analysis.

Słowa kluczowe

EN

text mining; Industry 4.0; information and communication technology; scientific paper

PL

eksploracja tekstu; Przemysł 4.0; technologie informacyjne i komunikacyjne; praca naukowa

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
• Rocznik: 2020
• Tom: z. 142
• Strony: 45--57
• Opis fizyczny: Bibliogr. 12 poz.

Twórcy

autor

Zdonek Dariusz

• Silesian University of Technology, Faculty of Organization and Management, Zabrze

Bibliografia

• 1. Bortolini, M., Ferrari, E., Gamberi, M., Pilati, F., and Faccio, M. (2017). Assembly system design in the Industry 4.0 era: a general framework. IFAC-PapersOnLine, 50(1), pp. 5700-5705.
• 2. Bradley, J., Loucks, J., Macaulay, J., Noronha, A., and Wade, M. (2015). Digital vortex: How digital disruption is redefining industries. Global Center for Digital Business Transformation: An IMD and Cisco initiative, p. 6-16. Available online https://www.cisco.com/c/dam/en/us/solutions/collateral/industry-solutions/digital-vortex-report.pdf, 29.10.2019.
• 3. Fan, W., Wallace, L., Rich, S., and Zhang, Z. (2006). Tapping the power of text mining. Communications of the ACM, 49(9), pp. 76-82.
• 4. Internet source: Apple Special Event. https://www.apple.com/apple-events/. Internet source: Google I/O '19 Recap https://events.google.com/io/recap/, 29.10.2019.
• 5. Future Manufacturing Technologies. https://www.fmtexpo.org/, Industries 4.0 http://www.industries4pointzero.com/conference/page/TC, Smart Industry Expo http://www.smartindustry-expo.com/, 29.10.2019.
• 6. Moeuf, A., Pellerin, R., Lamouri, S., Tamayo-Giraldo, S., and Barbaray, R. (2018). The industrial management of SMEs in the era of Industry 4.0. International Journal of Production Research, 56(3), pp. 1118-1136.
• 7. Nakagawa, T., and Uchimoto, K. (2007, June). A hybrid approach to word segmentation and pos tagging. Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions, pp. 217-220.
• 8. Trappey, A.J., Trappey, C.V., Govindarajan, U.H., Chuang, A.C., and Sun, J.J. (2017). A review of essential standards and patent landscapes for the Internet of Things: A key enabler for Industry 4.0. Advanced Engineering Informatics, 33, pp. 208-229.
• 9. Schumacher, A., Erol, S., and Sihn, W. (2016). A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises. Procedia CIRP 52, pp. 161-166. doi:10.1016/j.procir.2016.07.040.
• 10. Saturno M., Pertel V.M., Deschamps F., and Loures, E. (2017). Proposal of an automation solutions architecture for industry 4.0. Proceedings of the 24th International Conference on Production Research. Poznań: ICPR.
• 11. Silge, J., and Robinson, D. (2017). Text mining with R: A tidy approach. O'Reilly Media, Inc. Available online https://www.tidytextmining.com/index.html, 10.11.2019.
• 12. Vijayarani, S., Ilamathi, J., Nithya, and Phil, M. (2015). Preprocessing Techniques for Text Mining - An Overview. International Journal of Computer Science & Communi-cation Networks, 5(1), pp. 7-16.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).