Abstract
Nowadays, one of the most discussed topics in the technology industry is related to the new industrial revolution, called Industry 4.0. Industry 4.0 will transform entire production systems and products. However, the subject still lacks systematic study in its state of the art. This study seeks to identify relations or associations among emerging technologies in Industry 4.0. Through publications on its theme and keywords, a data mining technique was applied to help identify the network of associations with a new bibliometric approach. In order to reach the objective of the study, we utilized the Apriori algorithm in the Waikato Environment for Knowledge Analysis software. In this process, 15 association rules were found that met the input metrics: support, confidence, and lift. The rules point to two main technologies, internet of things and cyber-physical systems. This research points out that these technologies are key elements of Industry 4.0, and are related to others, such as cloud, big data, automation, virtualization, and robotics. Through data mining, the best associations and relations of the technologies in Industry 4.0 were identified. Moreover, this study pointed out the most important technologies for the new industrial revolution and the complementary technologies of each identified group. Thus, this network of technologies provides a basic guide for future works, which seek to deepen the characteristics of these relations.
Similar content being viewed by others
References
Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. Paper presented at the proceedings of the 20th international conference on very large data bases, VLDB.
Ahuett-Garza, H., & Kurfess, T. (2018). A brief discussion on the trends of habilitating technologies for Industry 4.0 and Smart manufacturing. Manufacturing Letters, 15, 60–63.
Amarasiri, R., Ceddia, J., & Alahakoon, D. (2005). Exploratory data mining lead by text mining using a novel high dimensional clustering algorithm. Paper presented at the fourth international conference on machine learning and applications (ICMLA’05).
Chen, G., & Xiao, L. (2016). Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methods. Journal of Informetrics, 10(1), 212–223.
Cheng, M., Xu, K., & Gong, X. (2016). Research on audit log association rule mining based on improved Apriori algorithm. In 2016 IEEE international conference on big data analysis (ICBDA) (pp. 1–7). IEEE.
Christen, P., & Goiser, K. (2007). Quality and complexity measures for data linkage and deduplication. In F. Guillet & H. J. Hamilton (Eds.), Quality measures in data mining (pp. 127–151). New York: Springer.
Chukwuekwe, D. O., Schjølberg, P., Rødseth, H., & Stuber, A. (2016). Reliable, robust and resilient systems: Towards development of a predictive maintenance concept within the industry 4.0 environment. Paper presented at the EFNMS Euro maintenance conference.
De Felice, F., Petrillo, A., & Zomparelli, F. (2018). A bibliometric multicriteria model on smart manufacturing from 2011 to 2018. IFAC-PapersOnLine, 51(11), 1643–1648.
del Pilar Angeles, M., & Perez-Franco, L. F. (2015). Analysis of string encoding functions during de-duplication process. Paper presented at the 2015 international conference on informatics, electronics & vision (ICIEV).
Ding, Y., Chowdhury, G. G., & Foo, S. (2001). Bibliometric cartography of information retrieval research by using co-word analysis. Information Processing and Management, 37(6), 817–842.
Ellegaard, O. (2018). The application of bibliometric analysis: Disciplinary and user aspects. Scientometrics, 116(1), 181–202.
Fatorachian, H., & Kazemi, H. (2018). A critical investigation of Industry 4.0 in manufacturing: theoretical operationalisation framework. Production Planning and Control, 29(8), 633–644.
Ghobakhloo, M. (2018). The future of manufacturing industry: A strategic roadmap toward Industry 4.0. Journal of Manufacturing Technology Management, 29(6), 910–936.
Glänzel, W., & Thijs, B. (2011). Using ‘core documents’ for the representation of clusters and topics. Scientometrics, 88(1), 297–309.
Hamidi, S. R., Aziz, A. A., Shuhidan, S. M., Aziz, A. A., & Mokhsin, M. (2018). SMEs maturity model assessment of IR4. 0 digital transformation. Paper presented at the international conference on Kansei engineering and emotion research.
Han, K. J., Kim, S., & Narayanan, S. S. (2007). Robust speaker clustering strategies to data source variation for improved speaker diarization. Paper presented at the 2007 IEEE workshop on automatic speech recognition & understanding (ASRU).
Han, J., Pei, J., & Kamber, M. (2011). Data mining: Concepts and techniques. Amsterdam: Elsevier.
Hashimi, H., Hafez, A., & Mathkour, H. (2015). Selection criteria for text mining approaches. Computers in Human Behavior, 51, 729–733.
He, Q. P., & Wang, J. (2018). Statistical process monitoring as a big data analytics tool for smart manufacturing. Journal of Process Control, 67, 35–43.
Hermann, M., Pentek, T., & Otto, B. (2016). Design principles for industrie 4.0 scenarios. Paper presented at the 2016 49th Hawaii international conference on system sciences (HICSS).
Huai, C., & Chai, L. (2016). A bibliometric analysis on the performance and underlying dynamic patterns of water security research. Scientometrics, 108(3), 1531–1551.
Janmaijaya, M., Shukla, A., Abraham, A., & Muhuri, P. (2018). A scientometric study of neurocomputing publications (1992–2018): An aerial overview of intrinsic structure. Publications, 6(3), 32.
Jerman, A., Pejić Bach, M., & Bertoncelj, A. (2018). A bibliometric and topic analysis on future competences at smart factories. Machines, 6(3), 41.
Junior, J. A. G., Busso, C. M., Gobbo, S. C. O., & Carreão, H. (2018). Making the links among environmental protection, process safety, and industry 4.0. Process Safety and Environmental Protection, 117, 372–382.
Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2018). Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives. Process Safety and Environmental Protection, 117, 408–425.
Kang, H. S., Lee, J. Y., Choi, S., Kim, H., Park, J. H., Son, J. Y., et al. (2016). Smart manufacturing: Past research, present findings, and future directions. International Journal of Precision Engineering and Manufacturing-Green Technology, 3(1), 111–128.
Kaur, J., & Gupta, V. (2010). Effective approaches for extraction of keywords. International Journal of Computer Science Issues (IJCSI), 7(6), 144.
Khamphakdee, N., Benjamas, N., & Saiyod, S. (2014). Network traffic data to ARFF converter for association rules technique of data mining. In 2014 IEEE conference on open systems (ICOS) (pp. 89–93). IEEE.
Kotsiantis, S. B., Kanellopoulos, D., & Pintelas, P. E. (2006). Data preprocessing for supervised leaning. International Journal of Computer Science, 1(2), 111–117.
Kyurkchiev, H., & Kaloyanova, K. (2016). Oracle and vertica for frequent itemset mining. Paper presented at the international conference on data mining and big data.
Larose, D. T., & Larose, C. D. (2014). Discovering knowledge in data: An introduction to data mining. New York: Wiley.
Lausch, A., Schmidt, A., & Tischendorf, L. (2015). Data mining and linked open data: New perspectives for data analysis in environmental research. Ecological Modelling, 295, 5–17.
Li, Q., Tang, Q., Chan, I., Wei, H., Pu, Y., Jiang, H., et al. (2018). Smart manufacturing standardization: Architectures, reference models and standards framework. Computers in Industry, 101, 91–106.
Liao, Y., Deschamps, F., Loures, E. D. F. R., & Ramos, L. F. P. (2017). Past, present and future of Industry 4.0: A systematic literature review and research agenda proposal. International Journal of Production Research, 55(12), 3609–3629.
Lu, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6, 1–10.
Lu, Y., & Cecil, J. (2016). An Internet of Things (IoT)-based collaborative framework for advanced manufacturing. The International Journal of Advanced Manufacturing Technology, 84(5–8), 1141–1152.
Mehmood, A., Choi, G. S., von Feigenblatt, O. F., & Park, H. W. (2016). Proving ground for social network analysis in the emerging research area “Internet of Things” (IoT). Scientometrics, 109(1), 185–201.
Merigó, J. M., Pedrycz, W., Weber, R., & de la Sotta, C. (2018). Fifty years of Information Sciences: A bibliometric overview. Information Sciences, 432, 245–268.
Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2017). Smart manufacturing: characteristics, technologies and enabling factors. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 233, 1342–1361. https://doi.org/10.1177/0954405417736547.
Moeuf, A., Pellerin, R., Lamouri, S., Tamayo-Giraldo, S., & Barbaray, R. (2018). The industrial management of SMEs in the era of Industry 4.0. International Journal of Production Research, 56(3), 1118–1136.
Muhuri, P. K., Shukla, A. K., & Abraham, A. (2019). Industry 4.0: A bibliometric analysis and detailed overview. Engineering Applications of Artificial Intelligence, 78, 218–235.
Muhuri, P. K., Shukla, A. K., Janmaijaya, M., & Basu, A. (2018). Applied soft computing: A bibliometric analysis of the publications and citations during (2004–2016). Applied Soft Computing, 69, 381–392.
Müller, J. M., Kiel, D., & Voigt, K.-I. (2018). What drives the implementation of Industry 4.0? The role of opportunities and challenges in the context of sustainability. Sustainability, 10(1), 247.
Pan, Z., Zhang, Y., & Huang, J. (2010). A library catalogue system using soundexing retrieval. World Review of Science, Technology and Sustainable Development, 7(1–2), 24–32.
Park, S.-T., Lee, S.-W., & Ko, M.-H. (2018). Industry 4.0 on keyword network analysis. Journal of Engineering and Applied Sciences, 13, 2442–2446.
Perianes-Rodriguez, A., Waltman, L., & van Eck, N. J. (2016). Constructing bibliometric networks: A comparison between full and fractional counting. Journal of Informetrics, 10(4), 1178–1195.
Piccarozzi, M., Aquilani, B., & Gatti, C. (2018). Industry 4.0 in management studies: A systematic literature review. Sustainability, 10(10), 3821.
Qi, Q., & Tao, F. (2018). Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access, 6, 3585–3593.
Rao, S. K., & Prasad, R. (2018). Impact of 5G technologies on Industry 4.0. Wireless Personal Communications, 100(1), 145–159.
Roblek, V., Meško, M., & Krapež, A. (2016). A complex view of industry 4.0. Sage Open, 6(2), 1–12. https://doi.org/10.1177/2158244016653987.
Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., et al. (2015). Industry 4.0: The future of productivity and growth in manufacturing industries. Boston Consulting Group, 9(1), 54–89.
Sanders, A., Elangeswaran, C., & Wulfsberg, J. (2016). Industry 4.0 implies lean manufacturing: research activities in industry 4.0 function as enablers for lean manufacturing. Journal of Industrial Engineering and Management, 9(3), 811–833.
Saucedo-Martínez, J. A., Pérez-Lara, M., Marmolejo-Saucedo, J. A., Salais-Fierro, T. E., & Vasant, P. (2017). Industry 4.0 framework for management and operations: A review. Journal of Ambient Intelligence and Humanized Computing, 9, 1–13.
Shao, Y., Liu, B., Wang, S., & Li, G. (2018). A novel software defect prediction based on atomic class-association rule mining. Expert Systems with Applications, 114, 237–254.
Shrouf, F., Ordieres, J., & Miragliotta, G. (2014). Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm. Paper presented at the 2014 IEEE international conference on industrial engineering and engineering management (IEEM).
Strozzi, F., Colicchia, C., Creazza, A., & Noè, C. (2017). Literature review on the ‘Smart Factory’concept using bibliometric tools. International Journal of Production Research, 55(22), 6572–6591.
Sun, L., Zhou, K., Zhang, X., & Yang, S. (2018). Outlier data treatment methods toward smart grid applications. IEEE Access, 6, 39849–39859.
Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157–169.
Trotta, D., & Garengo, P. (2018). Industry 4.0 key research topics: A bibliometric review. Paper presented at the 2018 7th international conference on industrial technology and management (ICITM).
Turčínek, P., & Turčínková, J. (2015). Exploring consumer behavior: Use of association rules. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 63(3), 1031–1042.
Uddin, S., & Khan, A. (2016). The impact of author-selected keywords on citation counts. Journal of Informetrics, 10(4), 1166–1177.
Ur-Rahman, N., & Harding, J. A. (2012). Textual data mining for industrial knowledge management and text classification: A business oriented approach. Expert Systems with Applications, 39(5), 4729–4739.
Waltman, L., Van Eck, N. J., & Noyons, E. C. (2010). A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics, 4(4), 629–635.
Wan, J., Cai, H., & Zhou, K. (2015). Industrie 4.0: Enabling technologies. Paper presented at the 2014 international conference on intelligent computing and internet of things (ICIT).
Wang, W., Laengle, S., Merigó, J. M., Yu, D., Herrera-Viedma, E., Cobo, M. J., et al. (2018). A bibliometric analysis of the first twenty-five years of the international journal of uncertainty, fuzziness and knowledge-based systems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 26(02), 169–193.
Wang, S., Wan, J., Li, D., & Zhang, C. (2016). Implementing smart factory of Industrie 4.0: An outlook. International Journal of Distributed Sensor Networks, 12(1), 3159805.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data mining: Practical machine learning tools and techniques. Los Altos: Morgan Kaufmann.
Yabing, J. (2013). Research of an improved Apriori algorithm in data mining association rules. International Journal of Computer and Communication Engineering, 2(1), 25.
Yafi, E., Al-Hegami, A. S., Alam, M. A., & Biswas, R. (2012). YAMI: Incremental mining of interesting association patterns. International Arab Journal of Information Technology, 9(6), 504–510.
Zheng, P., Sang, Z., Zhong, R. Y., Liu, Y., Liu, C., Mubarok, K., et al. (2018). Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives. Frontiers of Mechanical Engineering, 13(2), 137–150.
Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of industry 4.0: A review. Engineering, 3(5), 616–630.
Zhu, Y., & Yan, E. (2016). Searching bibliographic data using graphs: A visual graph query interface. Journal of Informetrics, 10(4), 1092–1107.
Acknowledgements
This paper was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Da Costa, M.B., Dos Santos, L.M.A.L., Schaefer, J.L. et al. Industry 4.0 technologies basic network identification. Scientometrics 121, 977–994 (2019). https://doi.org/10.1007/s11192-019-03216-7
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-019-03216-7