DATA ENGINEERING IN CRISP-DM PROCESS PRODUCTION DATA – CASE STUDY
Jolanta BRZOZOWSKA
d562@pollub.edu.pl(Poland)
Jakub PIZOŃ
(Poland)
https://orcid.org/0000-0002-0806-6771
Gulzhan BAYTIKENOVA
(Kazakhstan)
Arkadiusz GOLA
(Poland)
Alfiya ZAKIMOVA
(Kazakhstan)
https://orcid.org/0000-0003-0413-0542
Katarzyna PIOTROWSKA
Lublin University of Technology, Faculty of Mechanical Engineering, Department of Production Computerisation and Robotisation (Poland)
Abstract
The paper describes one of the methods of data acquisition in data mining models used to support decision-making. The study presents the possibilities of data collection using the phases of the CRISP-DM model for an organization and presents the possibility of adapting the model for analysis and management in the decisionmaking process. The first three phases of implementing the CRISP-DM model are described using data from an enterprise with small batch production as an example. The paper presents the CRISP-DM based model for data mining in the process of predicting assembly cycle time. The developed solution has been evaluated using real industrial data and will be a part of methodology that allows to estimate the assembly time of a finished product at the quotation stage, i.e., without the detailed technology of the product being known.
Keywords:
data engineering, data mining, CRISP-DM, assembly, process planningReferences
Ayele, W.Y. (2020). Adapting CRISP-DM for idea mining a data mining process for generating ideas using a textual dataset. International Journal of Advanced Computer Science and Applications, 11,(6), 20–32. https://doi.org/10.14569/IJACSA.2020.0110603
DOI: https://doi.org/10.14569/IJACSA.2020.0110603
Google Scholar
Brzozowska, J., Gola, A. (2021). Computer aided assembly planning using MS Excel software – a case study. Applied Computer Science, 17(2), 70-89. https://doi.org/10.23743/acs-2021-14
DOI: https://doi.org/10.35784/acs-2021-14
Google Scholar
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., and Wirth, R. (2000). CRISP-DM 1.0. Step-by-step data mining guide. SPSS. https://maestria-datamining-2010.googlecode.com/svnhistory/r282/trunk/dmct-teorica/tp1/CRISPWP-0800.pdf
Google Scholar
Cheng, A. (2023), Evaluating Fintech insdustry’s risks: A preliminary analysis based on CRISP-DM framework. Finance Research Letters, 55(B), 103966. https://doi.org/10.1016/j.frl.2023.103966
DOI: https://doi.org/10.1016/j.frl.2023.103966
Google Scholar
Choudhary, A.K., Harding, J.A., Popplewell, K. (2006). Knowledge discovery for moderating collaborative projects. 4th IEEE International Conference on Industrial Informatics, (pp. 519–524). IEEE. https://doi.org/10.1109/INDIN.2006.275610
DOI: https://doi.org/10.1109/INDIN.2006.275610
Google Scholar
Frawley, W., Piatetsky-Shapiro, G., & Matheus, C. (1992). Knowledge Discovery in Databases: An Overview. AI Magazine, 13(2), 57. https://doi.org/10.1609/aimag.v13i3.1011
Google Scholar
Gröger, C., Niedermann, F., & Mitschang B. (2012). Data mining-driven manufacturing process optimization. World congress on engineering, 14461305.
Google Scholar
Han J., Kamber M., Pei J. (2011). Data Mining. Concepts and Techniques, Third Edition, The Morgan Kaufmann Series in Data Management Systems, San Francisco, CA. https://doi.org/10.1016/C2009-0- 61819-5
Google Scholar
Hastie, T., Tibshirani, R., Friedman, J. H. (2001). The elements of statistical learning: Data mining, inference, and prediction, Second Edition, Springer Series in Statistics, New York, NY. https://doi.org/10.1007/978-0-387-84858-7:.
Google Scholar
Huber, S., Wiemer, H., Schneider, D., Ihlenfeldt, S. (2018). DMME: Data mining methodology for engineering applications – a holistic extension to the CRISP-DM Model. Procedia CIRP, 79, 403-408, https://doi.org/10.1016/j.procir.2019.02.106
DOI: https://doi.org/10.1016/j.procir.2019.02.106
Google Scholar
Krcmar, H. (2015). Informationsmanagement. Springer Gabler, Berlin-Heidelberg.. https://doi.org/10.1007/978-3-662-45863-1
DOI: https://doi.org/10.1007/978-3-662-45863-1
Google Scholar
Laudon, K.C., Laudon J.P., & Schoder D. (2010). Wirtschaftsinformatik. Eine Einführung. Pearson Studium, München, Deutschland.
Google Scholar
Martinez-Plumed F., Contreras-Ochando, L., Ferri, C., Hernandez-Orallo, J., Kull, M., Lachiche, N., RamirezQuintana, M. J., Flach, P. (2019). CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories, IEEE Transactions on Knowledge and Data Engineering, 33(8), 3048-3061. . https://doi.org/10.1109/TKDE.2019.2962680
DOI: https://doi.org/10.1109/TKDE.2019.2962680
Google Scholar
Moutinho L., Huarng K.-H. (2015). Quantitative Modelling in Marketing and Management, World Scientific Publishing, Singapore.
DOI: https://doi.org/10.1142/9657
Google Scholar
Nisbet, R., Elder, J., Miner G. (2009). Handbook of Statistical Analysis and Data Mining Applications, Elsevier. https://doi.org/10.1016/B978-0-12-374765-5.X0001-0
DOI: https://doi.org/10.1016/B978-0-12-374765-5.X0001-0
Google Scholar
Rohanizadeh, S.S., Moghadam, M.B. (2009). A Proposed Data Mining Methodology and its Application to Industrial Procedures, Journal of Industrial Engineering, 37-50.
Google Scholar
Santos, M., Azevedo, C. (2005). Data Mining – Descoberta de Conhecimento em Bases de Dados. FCA Publisher, https://hdl.handle.net/1822/19136Schröer, C., Kruse, F., Gómez, J. C. M. (2021). A Systematic Literature Review of Applying CRISP-DM Process Model. Procedia Computer Science, 181, 526-534. https://doi.org/10.1016/j.procs.2021.01.199
DOI: https://doi.org/10.1016/j.procs.2021.01.199
Google Scholar
Shearer, C. (2000). The CRISP-DM Model: The New Blueprint for Data Mining, Journal of Data Warehousing, 5(4), 13-22.
Google Scholar
Smyth, P., Hand, D., & Mannila, H. (2001). Principles of Data Mining, The MIT Press, 026208290x.
Google Scholar
Sturm, J. (2000). Hurtownie danych. SQL Server 7.0, Przewodnik techniczny. APN PROMISE.
Google Scholar
Surma, J. (2009). Business Intelligence. Systemy wspomagania decyzji biznesowych. PWN, Warsaw.
Google Scholar
Weller, J., Roesmann, D., Eggert, S., Von Enzberg, S., Gräßler, I. &, Dumitrescu, R. (2023). Identification and prediction of standard times in machining for precision steel tubes through the usage of data analytics. Procedia CIRP, 119, 514-520. https://doi.org/10.1016/j.procir.2023.01.011
DOI: https://doi.org/10.1016/j.procir.2023.01.011
Google Scholar
Zaskórski, P., & Pałka, D. (2012). Data Mining in decision-making processes. Warsaw School of Information Technology. Scientific Journals. 143-161.
Google Scholar
Authors
Gulzhan BAYTIKENOVAKazakhstan
Authors
Arkadiusz GOLAPoland
Authors
Katarzyna PIOTROWSKALublin University of Technology, Faculty of Mechanical Engineering, Department of Production Computerisation and Robotisation Poland
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