DATA ENGINEERING IN CRISP-DM PROCESS PRODUCTION DATA – CASE STUDY
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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.
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References
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
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
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
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
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
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
Gröger, C., Niedermann, F., & Mitschang B. (2012). Data mining-driven manufacturing process optimization. World congress on engineering, 14461305.
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
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:.
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
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
Laudon, K.C., Laudon J.P., & Schoder D. (2010). Wirtschaftsinformatik. Eine Einführung. Pearson Studium, München, Deutschland.
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
Moutinho L., Huarng K.-H. (2015). Quantitative Modelling in Marketing and Management, World Scientific Publishing, Singapore. DOI: https://doi.org/10.1142/9657
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
Rohanizadeh, S.S., Moghadam, M.B. (2009). A Proposed Data Mining Methodology and its Application to Industrial Procedures, Journal of Industrial Engineering, 37-50.
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
Shearer, C. (2000). The CRISP-DM Model: The New Blueprint for Data Mining, Journal of Data Warehousing, 5(4), 13-22.
Smyth, P., Hand, D., & Mannila, H. (2001). Principles of Data Mining, The MIT Press, 026208290x.
Sturm, J. (2000). Hurtownie danych. SQL Server 7.0, Przewodnik techniczny. APN PROMISE.
Surma, J. (2009). Business Intelligence. Systemy wspomagania decyzji biznesowych. PWN, Warsaw.
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
Zaskórski, P., & Pałka, D. (2012). Data Mining in decision-making processes. Warsaw School of Information Technology. Scientific Journals. 143-161.
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