Data Mining for Knowledge Acquisition in Engineering Design

  • Yoko Ishino
  • Yan Jin
Part of the Massive Computing book series (MACO, volume 3)


Recently knowledge capturing in the design process using data mining techniques has attracted attentions from researchers. In this paper, we focus on how designers apply their knowledge and find ways to proceed with their design in given design situations. This design know-how is the knack for a design to be successful. In order to automatically capture the design know-how through design processes without overburdening designers, we 1) — introduced an object-oriented CAD system for data gathering, 2) — proposed a three-layer design process model to represent generic design processes, and 3) — developed a method, called Extended Dynamic Programming (EDP), to extract know-how knowledge from the gathered design process data. The effectiveness of the proposed approach has been demonstrated by a gear design prototype system Gear-CAD.


Data Mining Design Process Knowledge Acquisition Pattern Match Design Event 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Barr, D. and Mani, G., “Using Neural Nets to Manage Investments,” AI Expert, 16–21, February 1994.Google Scholar
  2. Chikada, T. and Yoshimura, M., et al.,“An off-line signature verification method based on a hidden Markov model using column images as features,” in Proc. 9th Biennial Conference of the International Graphonomics Society (IGS’99), pp. 79–82, 1999.Google Scholar
  3. Conklin, D., Fortier, S. and Glasgow, J., “Knowledge Discovery in Molecular Databases,” IEEE Transactions on Knowledge and Data Engineering, 5 (6), pp. 985–987, Dec 1993.CrossRefGoogle Scholar
  4. Fayyad, U. M., Djorgovski, S. G. and Weir, N., “Automating the Analysis and Cataloging of Sky Surveys,” in Advances in Knowledge Discovery and Data Mining, pp. 471–493, AAAI Press/ The MIT Press: California, 1996.Google Scholar
  5. Holder, L., Cook, D. and Djoko, S., “Substructure Discovery in the SUBDUE system,” in Proceedings of KDD-94: the AAAI-94 Workshop on Knowledge Discovery in Databases, pp. 169–180, 1994.Google Scholar
  6. Jin, Y. and Zhou, W., “Agent-based knowledge management for collaborative engineering,” in Proc. Design Engineering Technical Conferences (DETC’99) in ASME, 1999.Google Scholar
  7. Jin, Y., Zhao, L. and Raghunath, A., “ActivePROCESS: A process-driven and agent-based approach to collaborative engineering,” in Proc. Design Engineering Technical Conferences (DETC’99) in ASME, 1999.Google Scholar
  8. Krogh, A. and Brown, M., et al.,“Hidden Markov models in computational biology: Application to protein modeling,” Journal of Molecular Biology, Vol. 235, pp.1501–1531, 1994.Google Scholar
  9. Letourneau, S., Famili, F. and Matwin, S., “Data Mining to Predict Aircraft Component Replacement,” in Data Mining: A Long-Term Dream, IEEE Intelligent Systems, pp. 59–66, November/ December 1999.Google Scholar
  10. Myers, K. L., Zumel, N. B. and Garcia, P., “Automated capture of rationale for the detailed design process,” in Proc. The 11th Conference on Innovative Applications of Artificial Intelligence. (IAAI’99), 1999.Google Scholar
  11. Sakoe, H. and Chiba, S, S., “Dynamic programming algorithm optimization for spoken word recognition,” Readings in Speech Recognition, (Waibel, A. and Lee, K., Eds.) pp. 159–165. Morgan Kaufmann, San Mateo, California, 1990.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Yoko Ishino
    • 1
  • Yan Jin
    • 1
  1. 1.IMPACT Laboratory, Denney Research Building, Suite 101University of Southern CaliforniaLos AngelesUSA

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