Skip to main content
Log in

Data crystallization applied for designing new products

  • Published:
Journal of Systems Science and Systems Engineering Aims and scope Submit manuscript

Abstract

It is only the observable part of the real world that can be stored in data. For such incomplete and ill-structured data, data crystallizing aims at presenting the hidden structure among events including unobservable events. This is realized by data crystallization, where dummy items, corresponding to potential existence of unobservable events, are inserted to the given data. These dummy items and their relations with observable events are visualized by applying KeyGraph to the data with dummy items, like the crystallization of snow where dusts are involved in the formation of crystallization of water molecules. For tuning the granularity level of structure to be visualized, the tool of data crystallization is integrated with human’s process of understanding significant scenarios in the real world. This basic method is expected to be applicable for various real world domains where previous methods of chance-discovery lead human to successful decision making. In this paper, we apply the data crystallization with human-interactive annealing (DCHA) to the design of products in a real company. The results show its effect to industrial decision making.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Chance discovery Consortium. [Online] Available via DIALOG. http://www.chancediscovery.com/english/index.php

  2. Eris, O. (2004). Effective Inquiry for Innovative Engineering Design, Kluwer Academic Publisher

  3. Fruchter, R. Ohsawa Y. & Matsumura N. (2005). Knowledge reuse through chance discovery from an enterprise design-build enterprise data store. New Mathematics and Natural Computation, 3:393–406

    Article  Google Scholar 

  4. Gaver, W.W., Beaver, J. & Benford, S. (2003). Ambiguity as a Resource for Design. In: Proceedings of Computer Human Interactions

  5. Goldberg, D.E. (2002). The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Boston, MA: Kluwer Academic Publishers

    MATH  Google Scholar 

  6. Horie, K., Ohsawa, Y. & Okazaki, N. (2006). Products Designed on Scenario Maps Using Pictorial KeyGraph: WSEAS Transitions on Information Science and Applications, 3(7): 1324–1331, ISSN 1790-0832

    Google Scholar 

  7. Joshi, M., Kumar, V. & Agarwal, R. (2001). Evaluating boosting algorithms to classify rare classes: comparison and improvements. In: Proceedings of the First IEEE International Conference on Data Mining

  8. Maeno, Y. & Ohsawa, Y. (2006A). Understanding of dark events for Harnessing risk. In: Ohsawa, Y. and Tsumoto, S. (eds.), Chance Discoveries in Real World Decision Making, Springer-Verlag

  9. Maeno, Y. & Ohsawa, Y. (2006B). Human-computer interactive annealing for crystallization of invisible dark events. IEEE Transactions on Industrial Electronics, to appear.

  10. Matsumura, N., Matsuo, Y., Ohsawa, Y. & Ishizuka, N. (2002). discovering emerging topics from www. Journal of Contingencies and Crisis Management, 10(2): 73–81

    Article  Google Scholar 

  11. Ohsawa, Y. (2002). KeyGraph as risk explorer from earthquake sequence. Journal of Contingencies and Crisis Management, 10(3): 119–128

    Article  MathSciNet  Google Scholar 

  12. Ohsawa, Y. (2003). KeyGraph: visualized structure among event clusters. In: Y. Ohsawa and P. McBurney. (eds.), Chance Discovery, pp. 262–275, Springer Verlag

  13. Ohsawa, Y. (2005). Data crystallization: chance discovery extended for dealing with unobservable events. New Mathematics and Natural Science, 1: 373–392

    Article  MATH  Google Scholar 

  14. Ohswa, Y. (2006). Scenario maps on situational switch model, applied to blood-test data fro hepatitis c patients. In: Ohsawa, Y. and Tsumoto, S. (eds.), Chance Discoveries in Real World Decision Making, pp. 69–80, Springer

  15. Ohsawa, Y., Fujie, H., Saiura, A., Okazaki, N. & Matsumura, N. (2004). Process to discovering iron decrease as chance to use interferon to Hepatitis B. In: Paton, R. (ed.), Multidisciplinary Approaches to Theory in Medicine

  16. Ohsawa, Y. & Fukuda, H. (2002). Chance discovery by stimulated group of people — an application to understanding rare consumption of food. Journal of Contingencies and Crisis Management, 10(3): 129–138

    Article  Google Scholar 

  17. Ohsawa, Y. & McBurney, P. (eds.) (2003). Chance Discovery, Advanced Information Processing, pp. 2–15, Springer-Verlag

  18. Ohsawa, Y. & Nara, Y. (2003). Understanding internet users on double helical model of chance-discovery process. New Generation Computing, 21(2): 109–122

    Article  MATH  Google Scholar 

  19. Ohsawa, Y. & Tsumoto, S. (2006). Chance Discovery in Real World Decision Making. Series on Computational Intelligence, Springer-Verlag

  20. Ohsawa, Y. & Usui, M. (2006). Creative marketing as application of chance discovery. In: Ohsawa, Y. and Tsumoto, S. (eds.), Chance Discovery in Real World Decision Making, Computational Intelligence, pp. 253–272, Springer-Verlag

  21. Okazaki, N. & Ohsawa, Y. (2003). Polaris: an integrated data miner for chance discovery. In: Proceedings of the 3rd Int’l Workshop on Chance Discovery and Its Management, Greece

  22. Weiss, G.M. & Hirsh, H. (1998). Learning to predict rare events in event sequences. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), AAAI Press, Menlo Park, CA, 359–363

    Google Scholar 

  23. Yada, K., Motoda, H., Washio, T. & Miyawaki, A. (2005). Consumer behavior analysis by graph mining technique. New Mathematics and Natural Computation, 2(1): 59–68

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kenichi Horie.

Additional information

Kenichi Horie received M.S. degrees in Business administration from the University of Tsukuba. He is a graduate student at Graduate School of Engineering, The University of Tokyo. He has worked for Nagase & Co., Ltd. His research interests lie in chance discovery, human recognition, design, and technology innovation. He is a member of JSQC, IEIEC, JASMIN, JCS, KMSJ and JASVE.

Yoshiharu Maeno received the B.S. and M.S. degrees in physics from The University of Tokyo. He is a graduate student at Graduate School of System Management, University of Tsukuba. He has worked for NEC Corporation. His research interests are in chance discovery, human recognition, and design and technology innovation. He is a member of IEEE, IEICE and JSAI. He received Young Researchers Award from IEICE in 1999.

Yukio Ohsawa is an associate professor in the School of Engineering, The University of Tokyo. He moved from Graduate School of Business Sciences, University of Tsukuba, Tokyo, Japan in 2005. He received a Ph.D. in Communication and Information Engineering from The University of Tokyo. He initiated the research area of Chance Discovery as well as a series of international meetings (conference sessions and workshops), e.g., the fallsymposium of the American Association of Artificial Intelligence (2001). He edited the first book on Chance Discovery published by Springer Verlag and special issues in international and Japanese domestic journals. Chance discovery is growing: Journal issues have been published from the international journal on New Mathematics and Natural Computing, and new books are appearing.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Horie, K., Maeno, Y. & Ohsawa, Y. Data crystallization applied for designing new products. J. Syst. Sci. Syst. Eng. 16, 34–49 (2007). https://doi.org/10.1007/s11518-006-5027-1

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11518-006-5027-1

Keywords

Navigation