Application of a knowledge discovery model in the age of Big Data

Authors

DOI:

https://doi.org/10.47456/bjpe.v7i3.35743

Keywords:

Product Development, Knowledge Discovery, Big Data, Social Network

Abstract

The ease and evolution of technological access has been responsible for the speed and volume with which data is produced. As a result, scenarios, opportunities and challenges arise that favor decision-making and help the product development process (PDP). Therefore, the present work developed the model called MDC-PDP (Knowledge Discovery Model in the Product Development Process), aiming to support the knowledge discovery in the product development process. To support the model, traditional methodologies associated with Big Data demands were used. To illustrate its application, this model was applied in the application domain of the fashion industry. The model showed that efforts made in the early understanding of the data, can contribute to the extracted data being less comple. Another evidence is the dissociation between volume and data value, as the data value is not tied to its volume. Finally, the MDC-PDP also contributed to obtaining useful and new knowledge in the development of fashion collections, making it possible to apply the model in other application domains.

Downloads

Download data is not yet available.

Author Biographies

Emerson Rabelo, Instituto Federal do Paraná

Graduated in Computer Science from the State University of Maringá (UEM-2003); Master's in Computer Science from the State University of Maringá (UEM-2007). PhD in Production Engineering from the Graduate Program in Engineering at the Methodist University of Piracicaba (UNIMEP); He is currently a professor of exclusive dedication at the Federal Institute of Paraná. He has experience in Computer Science, working mainly on the following topics: Database, KDD, Data Mining, Big Data, software engineering, operating system, information system and distributed system.

Fernando Celso de Campos, UNIMEP – Universidade Metodista de Piracicaba - PPGEP/FEAU

He holds a degree in Computer Science from the Institute of Mathematical Sciences of São Carlos (ICMSC-USP) (1987), a Master's degree in Mechanical Engineering from the School of Engineering of São Carlos (EESC-USP) (1994), a PhD in Mechanical Engineering from the Faculty of São Carlos Engenharia (EESC-USP) (1999), post-doctorate in Production Engineering at DEP-UFSCar (2016). He is currently a professor and researcher at the Methodist University of Piracicaba-UNIMEP, working in undergraduate and graduate courses. He has experience in the area of ​​Production Engineering, with an emphasis on Applied Information Technology (information system, process modeling, IT governance, ERP, knowledge management), Digital Technologies (Business Intelligence, BIA, Big Data Analysis, IoT) , Green IT. Sustainability (Green IT, Circular Economy, Cleaner Production), Industry 4.0, Industrial Maintenance (PCM, Preventive, Predictive, Prescriptive, TPM), APL / cluster management / cooperation models, strategic operations management and project alternatives. He worked in Methods Engineering in continuous improvement projects (Lean Manufacturing, SMED, Lean Office, Lean Healthcare, Management of the use of technologies in the health area).

Leandro Magno Correa da Silva, IFPR - Instituto Federal do Paraná

Professor of basic, technical and technological education at IFPR - Federal Institute of Paraná. Master in Technological Innovations from UTFPR - Federal Technological University of Paraná. Specialist in Information Technology by SPEI - Sociedade Paranaense de Ensino em Informática. Bachelor in Computer Science from UEM - State University of Maringá - Professional Master's in Technological Innovations - Federal Technological University of Paraná, UTFPR. Experience in Computer Science in the topics: Software Development and Engineering, Algorithms, Operating Systems, Computer Architecture, Databases, Computer Networks, Distributed Systems and Artificial Intelligence.

References

Asamoah, D. A., & Sharda, R. (2015). Adapting CRISP-DM Process for Social Network Analytics: Application to Healthcare. Twenty-First Americas Conference on Information Systems, Puerto Rico, 2015, (Mdd), 1–12.

Begoli, E., & Horey, J. (2012). Design Principles for Effective Knowledge Discovery from Big Data. Proceedings of the Joint Working IEEE/IFIP Conference on Software Architecture and European Conference on Software Architecture, Helsinki, Finland, 2012. 215–218. https://doi.org/10.1109/WICSA-ECSA.212.32

Davenport, T. (2014). Big data at work: dispelling the myths, uncovering the opportunities. Harvard Business Review Press.

Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases. AI Magazine, 17(3), 37. https://doi.org/10.1609/aimag.v17i3.1230

Gantz, J., Reinsel, D., & Shadows, B. D. (2012). The Digital Universe in 2020. IDC iView “Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East,” 2007(December 2012), 1–16.

Halvorsen, K., Hoffmann, J., Coste-Manière, I., & Stankeviciute, R. (2013). Can fashion blogs function as a marketing tool to influence consumer behavior? Evidence from Norway. Journal of Global Fashion Marketing, 4(3), 211–224. https://doi.org/10.1080/20932685.2013.790707

Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Ullah Khan, S. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98–115. https://doi.org/10.1016/j.is.2014.07.006

Kaisler, S., Armour, F., Espinosa, J. A., & Money, W. (2013). Big data: Issues and challenges moving forward. In System Sciences (HICSS), Proceedings of the Hawaii International Conference, HI, USA, 46.

Li, J., Tao, F., Cheng, Y., & Zhao, L. (2015). Big Data in product lifecycle management. The International Journal of Advanced Manufacturing Technology, 81(1–4), 667–684. https://doi.org/10.1007/s00170-015-7151-x

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey & Company. Retrieved from https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Digital/Our%20Insights/Big%20data%20The%20next%20frontier%20for%20innovation/MGI_big_data_exec_summary.pdf

McAfee, A., & Brynjolfsson, E. (2012). Big Data. The management revolution. Harvard Buiness Review, 90(10), 61–68. https://doi.org/10.1007/s12599-013-0249-5

Milonas, E. (2011). Wittgenstein and web facets. NASKO, 3(1), 33–40.

Piatetsky, B. G. (2014). CRISP-DM, still the top methodology for analytics, data mining, or data science projects. Retrieved from http://www.kdnuggets.com/2014/10/crisp-dm-top-methodology-analytics-data-mining-data-science-projects.html

Rabelo, E., Dias, M., Franco, C., & Pacheco, R. C. S. (2008). Information visualization: Which the most appropriate technique to represent data mining results? Proceedings of the International Conference on Computational Intelligence for Modelling Control and Automation, CIMCA 2008, Vienna, Áustria. (pp. 1228–1233). https://doi.org/10.1109/CIMCA.2008.139

Schröer, C., Kruse, F., & Gómez, J. M. (2021). A Systematic Literature Review on Applying CRISP-DM Process Model. Procedia Computer Science, 181, 526-534.

Shiri, A. (2014). Making sense of big data: A facet analysis approach. Knowledge Organization, 41(5), 357–368. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84913553153&partnerID=tZOtx3y1

Wu, X., Zhu, X., Wu, G.-Q., & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107. https://doi.org/10.1109/TKDE.2013.109

Zhang, Y., Ren, S., Liu, Y., & Si, S. (2017). A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products. Journal of Cleaner Production, 142, 626–641. https://doi.org/10.1016/j.jclepro.2016.07.123

Zhuang, Y., Wang, Y., Shao, J., Chen, L., Lu, W., Sun, J., … Wu, J. (2016). D-Ocean: an unstructured data management system for data ocean environment. Frontiers of Computer Science, 10(2), 353–369. https://doi.org/10.1007/s11704-015-5045-6

Zikopoulos, P., Eaton, C., & et al. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media.

Downloads

Published

2021-08-17

How to Cite

Rabelo, E., Campos, F. C. de, & Silva, L. M. C. da. (2021). Application of a knowledge discovery model in the age of Big Data. Brazilian Journal of Production Engineering, 7(3), 106–125. https://doi.org/10.47456/bjpe.v7i3.35743

Issue

Vol. 7 No. 3 (2021): Número Regular (Julho - Setembro)

Section

PRODUCT ENGINEERING

License

Copyright (c) 2021 Brazilian Journal of Production Engineering - BJPE

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Atribuição-NãoComercial-CompartilhaIgual
CC BY-NC-SA

This license lets others remix, adapt, and build upon your work non-commercially, as long as they credit you and license their new creations under the identical terms.

Most read articles by the same author(s)