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Data engineering in CRISP-DM process production data – case study

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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 decision-making 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.
Słowa kluczowe
Rocznik
Strony
83--95
Opis fizyczny
Bibliogr. 23 poz., fig.
Twórcy
  • Lublin University of Technology, Faculty of Mechanical Engineering, Department of Production Computerisation and Robotisation
autor
  • Lublin University of Technology, Faculty of Management, Department of Enterprise Organization
  • School of Business and Entrepreneurship, D. Serikbayev East Kazakhstan Technical University, Kazakhstan
  • Lublin University of Technology, Faculty of Mechanical Engineering, Department of Production Computerisation and Robotisation
  • School of Business and Entrepreneurship, D. Serikbayev East Kazakhstan Technical University, Kazakhstan
  • Lublin University of Technology, Faculty of Mechanical Engineering, Department of Production Computerisation and Robotisation
Bibliografia
  • [1] 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
  • [2] 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
  • [3] 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/svn-history/r282/trunk/dmct-teorica/tp1/CRISPWP-0800.pdf
  • [4] 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
  • [5] 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
  • [6] 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
  • [7] Gröger, C., Niedermann, F., & Mitschang B. (2012). Data mining-driven manufacturing process optimization. World congress on engineering, 14461305.
  • [8] 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
  • [9] Hastie, T., Tibshirani, R., & Friedman, J. H. (2001). The elements of statistical learning: Data mining, inference, and prediction, Second Edition, Springer, New York, NY. https://doi.org/10.1007/978-0-387-84858-7
  • [10] 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
  • [11] Krcmar, H. (2015). Informationsmanagement. Springer, Berlin-Heidelberg. https://doi.org/10.1007/978-3-662-45863-1
  • [12] Laudon, K.C., Laudon J.P., & Schoder D. (2010). Wirtschaftsinformatik: Eine Einführung. Pearson, München, Deutschland.
  • [13] Martinez-Plumed F., Contreras-Ochando, L., Ferri, C., Hernandez-Orallo, J., Kull, M., Lachiche, N., Ramirez-Quintana, 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
  • [14] Moutinho L., Huarng K.-H. (2015). Quantitative modelling in marketing and management, World Scientific Publishing, Singapore.
  • [15] 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
  • [16] Rohanizadeh, S.S., Moghadam, M.B. (2009). A Proposed Data Mining Methodology and its Application to Industrial Procedures, Journal of Industrial Engineering, 37-50.
  • [17] 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
  • [18] Shearer, C. (2000). The CRISP-DM Model: The New Blueprint for Data Mining, Journal of Data Warehousing, 5(4), 13-22.
  • [19] Smyth, P., Hand, D., & Mannila, H. (2001). Principles of Data Mining, The MIT Press, 026208290x.
  • [20] Sturm, J. (2000). Hurtownie danych. SQL Server 7.0, Przewodnik techniczny. APN PROMISE.
  • [21] Surma, J. (2009). Business Intelligence. Systemy wspomagania decyzji biznesowych. PWN, Warsaw.
  • [22] 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
  • [23] Zaskórski, P., & Pałka, D. (2012). Data mining in decision-making processes. Warsaw School of Information Technology. Scientific Journals. 143-161.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6b927594-a5ea-45cd-a14b-2eea9f23ebc7
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