Hostname: page-component-848d4c4894-m9kch Total loading time: 0 Render date: 2024-05-27T13:30:32.077Z Has data issue: false hasContentIssue false
  • English
  • Français

MODELLING THE DESIGN OF MODELS: AN EXAMPLE USING CRISP-DM

Published online by Cambridge University Press:  19 June 2023

Udo Kannengiesser  and
John S. Gero
Udo Kannengiesser*
Affiliation:
Johannes Kepler University Linz;
John S. Gero
Affiliation:
University of North Carolina at Charlotte, USA
*
Kannengiesser, Udo, Johannes Kepler University Linz, Austria, udo.kannengiesser@jku.at

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Design is widely understood as a domain-independent notion, comprising any activity concerned with creating artefacts. This paper shows that models can be viewed as artefacts, and that the design of models resembles the design of artefacts in other domains. The function-behaviour-structure (FBS) ontology of design is applied to models, mapping generic characteristics of models derived from literature on modelling onto basic, design-ontological categories. An example of model design, namely the CRISP-DM model for designing data mining models, is analysed and compared with models of designing in other domains (systems engineering, mechanical engineering, software engineering, and service design). The results show that there are fundamental commonalities but also differences, revealing the need for further research in developing a theory of model design.

Keywords

Design theoryProduct modelling / modelsOntologiesDesign methodologyModel design
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 2705 - 2714
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

References

Browning, T.R. (2010), “On the alignment of the purposes and views of process models in project management”, Journal of Operations Management, Vol. 28 No. 4, pp. 316–332. https://doi.org/10.1016/j.jom.2009.11.007CrossRefGoogle 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, CRISP-DM Consortium.Google Scholar
Churchill, D. (2013), “Conceptual model design and learning uses”, Interactive Learning Environments, Vol. 21 No. 1, pp. 54–67. http://dx.doi.org/10.1080/10494820.2010.547203CrossRefGoogle Scholar
Eckert, C. and Hillerbrand, R. (2018), Models in Engineering Design: Generative and Epistemic Function of Product Models, In: Vermaas, P. and Vial, S. (Eds), Advancements in the Philosophy of Design, Springer, Cham. https://doi.org/10.1007/978-3-319-73302-9_11Google Scholar
Eckert, C. and Hillerbrand, R. (2022), “Models in engineering design as decision-making aids”, Engineering Studies, Vol. 14 No. 2, pp. 134–157. https://doi.org/10.1080/19378629.2022.2129061CrossRefGoogle Scholar
El-Haik, B. and Roy, D.M. (2005), Service Design for Six Sigma: A Roadmap for Excellence, John Wiley & Sons, Hoboken, NJ.CrossRefGoogle Scholar
Gero, J.S. (1990), “Design prototypes: a knowledge representation schema for design”, AI Magazine, Vol. 11 No. 4, pp. 26–36. https://doi.org/10.1609/aimag.v11i4.854Google Scholar
Gero, J.S. and Kannengiesser, U. (2007), “A function–behavior–structure ontology of processes, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol. 21 No. 4, pp. 379–391. https://doi.org/10.1017/S0890060407000340CrossRefGoogle Scholar
Gero, J.S. and Kannengiesser, U. (2004), “The situated function-behaviour-structure framework”, Design Studies, Vol. 25 No. 4, pp. 373–391. https://doi.org/10.1016/j.destud.2003.10.010CrossRefGoogle Scholar
Gero, J.S. and Kannengiesser, U. (2014), “The function-behaviour-structure ontology of design”, In: Chakrabarti, A. and Blessing, L.T.M. (Eds), An Anthology of Theories and Models of Design, Springer, pp. 263–283.CrossRefGoogle Scholar
Guizzardi, G. and Proper, H.A. (2021), “On understanding the value of domain modeling”, Proceedings of the International Workshop on Value Modelling and Business Ontologies, Bolzano, Italy, pp. 51–62.Google Scholar
Harrington, M.E. (2020), The Design of Experiments in Neuroscience, Cambridge University Press, Cambridge.CrossRefGoogle Scholar
Hotie, F. and Gordijn, J. (2019), “Value-based process model design”, Business & Information Systems Engineering, Vol. 61, pp. 163–180. https://doi.org/10.1007/s12599-017-0496-yCrossRefGoogle Scholar
INCOSE (2015), Systems Engineering Handbook: A Guide for System Life Cycle Processes and Activities, Fourth Edition, INCOSE-TP-2003-002-04, International Council on Systems Engineering (INCOSE), San Diego, CA.Google Scholar
Kannengiesser, U. and Gero, J.S. (2022), “What distinguishes a model of systems engineering from other models of designing? An ontological, data-driven analysis”, Research in Engineering Design, Vol. 33, pp. 129–159. https://doi.org/10.1007/s00163-021-00382-9CrossRefGoogle Scholar
Kaschek, R. (2018), “20 years after: What in fact is a model?”, Enterprise Modelling and Information Systems Architectures (EMISAJ), Vol. 13, pp. 28–34. https://doi.org/10.18417/emisa.si.hcm.2Google Scholar
Krogstie, J., Lindland, O.I. and Sindre, G. (1995), “Defining quality aspects for conceptual models”, In: Falkenberg, E.D., Hesse, W. and Olivé, A. (Eds), Information System Concepts, IFIP Advances in Information and Communication Technology, Springer, Boston, MA, pp. 216–231. https://doi.org/10.1007/978-0-387-34870-4_22CrossRefGoogle Scholar
Kropp, Y. and Thalheim, B. (2017), “Data mining design and systematic modelling”, In: XIX International Conference Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL'2017), Moscow, pp. 273–280.Google Scholar
Kruchten, P. (2004), The Rational Unified Process: An Introduction, Addison-Wesley, Upper Saddle River, NJ.Google Scholar
Martinez-Plumed, F., Contreras-Ochando, L., Ferri, C., Hernandez-Orallo, J., Kull, M., Lachiche, N., Ramirez-Quintana, M.J. and Flach, P. (2021), “CRISP-DM twenty years later: From data mining processes to data science trajectories”, IEEE Transactions on Knowledge and Data Engineering, Vol. 33 No. 8, pp 3048–3061. https://doi.org/10.1109/TKDE.2019.2962680CrossRefGoogle Scholar
McAdams, D.A. and Dym, C.L. (2004), “Modeling and information in the design process”, Proceedings of the ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Volume 3a: 16th International Conference on Design Theory and Methodology, Salt Lake City, Utah, pp. 21–30. https://doi.org/10.1115/DETC2004-57101CrossRefGoogle Scholar
Nöth, W. (2018), “The semiotics of models”, Sign Systems Studies, Vol. 46 No. 1, pp. 7–43. https://doi.org/10.12697/SSS.2018.46.1.01CrossRefGoogle Scholar
Ritchey, T. (2010), “Outline for a morphology of modelling methods: Contribution to a general theory of modelling”, Acta Morphologica Generalis, Vol. 1 No. 1.Google Scholar
Pahl, G. and Beitz, W. (2007), Engineering Design: A Systematic Approach, Springer, Berlin.CrossRefGoogle Scholar
Reijers, H.A. (2021), “Business process management: The evolution of a discipline”, Computers in Industry, Vol. 126, pp. 1–5. https://doi.org/10.1016/j.compind.2021.103404CrossRefGoogle Scholar
Roozenburg, N.F.M. (1993), “On the pattern of reasoning in innovative design”, Design Studies, Vol. 14 No. 1, pp. 4−18. https://doi.org/10.1016/S0142-694X(05)80002-XCrossRefGoogle Scholar
Rothenberg, J. (1989), “The Nature of Modeling”, In: Widman, L.E., Loparo, K.A. and Nielson, N. (Eds), Artificial Intelligence, Simulation & Modeling, John Wiley & Sons, New York, pp. 75–92.Google Scholar
Saltz, J.S. (2021), CRISP-DM for data science: Strengths, weaknesses and potential next steps, 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, pp. 2337–2344. https://doi.org/10.1109/BigData52589.2021.9671634CrossRefGoogle Scholar
Stachowiak, H. (1973), Allgemeine Modelltheorie, Springer-Verlag, Wien, New York.CrossRefGoogle Scholar
Thalheim, B. (2010), “Towards a theory of conceptual modelling”, Journal of Universal Computer Science, Vol. 16 No. 20, pp. 3102–3137. https://doi.org/10.3217/jucs-016-20-3102Google Scholar
Thalheim, B. (2011), “The theory of conceptual models, the theory of conceptual modelling and foundations of conceptual modelling”, In: Embley, D. and Thalheim, B. (Eds), Handbook of Conceptual Modeling, Springer, Berlin, Heidelberg, pp. 543–577. https://doi.org/10.1007/978-3-642-15865-0_17CrossRefGoogle Scholar
Army, US, (2010), Field Manual 5.0 The Operations Process, Headquarters of the U. S. Army.Google Scholar
Wirth, R. and Hipp, J. (2000), “CRISP-DM: Towards a standard process model for data mining”, Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, Manchester, UK, pp. 29–39.Google Scholar
Zott, C. and Amit, R. (2010), “Business model design: An activity system perspective”, Long Range Planning, Vol. 43 No. 2–3, pp. 216–226. https://doi.org/10.1016/j.lrp.2009.07.004CrossRefGoogle Scholar