Mining Authoritativeness of Collaborative Innovation Partners

Authors

  • Joseph Engler Adaptive Systems Rockwell Collins, Inc. Cedar Rapids, IA 52498 USA
  • Andrew Kusiak Mechanical and Industrial Engineering 3131 Seamans Center University of Iowa Iowa City, IA 52242-1527 USA

Keywords:

Innovation, web mining, text mining

Abstract

The global marketplace over the past decade has called for innovative products and cost reduction. This perplexing duality has led companies to seek external collaborations to effectively deliver innovative products to market. External collaboration often leads to innovation at reduced research and development expenditure. This is especially true of companies which find the most authoritative entity (usually a company or even a person) to work with. Authoritativeness accelerates development and research-to-product transformation due to the inherent knowledge of the authoritative entity. This paper offers a novel approach to automatically determine the authoritativeness of entities for collaboration. This approach automatically discovers an authoritative entity in a domain of interest. The methodology presented utilizes web mining, text mining, and generation of an authoritativeness metric. The concepts discussed in the paper are illustrated with a case study of mining the authoritativeness of collaboration partners for microelectromechanical systems (MEMS).

References

A. Kusiak, Innovation: A Data-Driven Approach, International Journal of Production Economics, Vol. 122, No. 1, pp. 440-448, 2009. http://dx.doi.org/10.1016/j.ijpe.2009.06.025

G. Berkhout, P. van der Duin, Mobile Data Innovation: Lucio and the Cyclic Innovation Model, Proc. of the 6th Intl. Conf. on Electorinc Commerce, Delft, Netherlands, pp. 603-608, 2004.

Y. Sawatani, F. Nakarmura, A. Sakakibara, M. Hoshi, S. Masuda, Innovation Patterns, Proc. of the 2007 IEEE Intl. Conf. on Services and Computing, Salt Lake City, UT, pp. 427-434, July 2007. http://dx.doi.org/10.1109/scc.2007.71

J.B. Zhang, Y. Tao,The Interaction Based Innovation Process of Architectural Design Service, Industrial Engineering and Engineering Management 2007 IEEE Intl. Conf., pp.1719 - 1723, Dec. 2007.

A.W. Ulwick, Turn Customer Input Into Innovation. Harvard Business Review, Vol. 80, No. 1, pp. 91-97, 2002.

L. Collins, Opening up the Innovation Process, Engineering Management Journal, Vol. 16, No. 1, pp. 14-17, 2006. http://dx.doi.org/10.1049/em:20060102

A. Langville, C. Meyer, Deeper Inside PageRank, Internet Mathematics, Vol. 1, No. 3, pp. 335-380. http://dx.doi.org/10.1080/15427951.2004.10129091

B. Liu, Web Data Mining, Springer, Heidelberg, 2007.

T. Haveliwala, Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search, IEEE Transactions on Knowledge and Data Engineering, Vol. 15, No. 4, pp. 784-796, 2003. http://dx.doi.org/10.1109/TKDE.2003.1208999

S. Kamvar, T. Haveliwala, C. Manning, G. Golub, Extrapolation Methods for Accelerating PageRank Computations, Proc. Of the 12th Intl. Conf. on World Wide Web, Budapest, Hungary, pp. 261-270, 2003. http://dx.doi.org/10.1145/775152.775190

I. Whitten, E. Frank, Data Mining, Practical Machine Learning Tools and Techniques, Morgan Kauffman, New York, 2005.

J. Han, Y. Yin, G. Dong, Efficient Mining of Partial Periodic Patterns in Time Series Databases, Proc. of the 15th IEEE Intl. Conf. on Data Engineering, Sydney, Australia, pp. 106-115, March 1999.

S. Simon, Modeling and Design Aspects of the MEMS Switch, Proc. Of the 2003 IEEE International Semiconductor Conference, Sinaia, Romania, September 28 - October 2, pp. 128-132, 2003. http://dx.doi.org/10.1109/smicnd.2003.1251360

R. Maeda, M. Takahashi, S. Sasaki, Commercialization of MEMS and Nano Manufacturing, Proc. Of the 6th IEEE Intl. Conf. On Polymers and Adhesives in Microelectronics and Photonics, Tokyo, Japan, pp. 20-23, January 2007. http://dx.doi.org/10.1109/polytr.2007.4339130

G. M. Rebeiz, Homepage htt p : ==www:eecs:umich:edu=rebeiz=rebeiz:html.

C. L. Goldsmith, Homepage htt p : ==www:memtronics:com=page:aspx?pageid = 10.

Y. Zhai, B. Liu,Web Data Extraction Based on Partial Tree Alignment, Proc. of the 2005 International World Wide Web Conference, May 10-14. Chiba, Japan, pp. 76-85, 2005. http://dx.doi.org/10.1145/1060745.1060761

L. Shih, D. Karger, Using URLs and Table Layout for Web Classification Tasks, Proc. of WWW 2004, May 17-22, New York, pp. 193-202, 2004. http://dx.doi.org/10.1145/988672.988699

B. Kules, J. Kustanowitz, B. Shneiderman, Categorizing Web Search Results into Meaningful and Stable Categories Using Fast-Feature Techniques, Proc. of JCDL'06, June 11-15, pp. 210-219, 2006. http://dx.doi.org/10.1145/1141753.1141801

X. Jin, R. Li, X. Shen, R. Bie, Automatic Web Pages Categorization with ReliefF and Hidden Nad've Bayes, Proc. of SAC '07, March 11-15, pp. 617-621, 2007. http://dx.doi.org/10.1145/1244002.1244143

J. Engler, A. Kusiak, A. Mining the Requirements for Innovation, Mechanical Engineering, Vol. 130, No. 11, pp. 38-40, 2008.

P. Chapman et al., Step-by-step Data Mining Guide, CRSIP-DM Consortium, CRISP-DM 1.0, 2000.

N. Lavrac, H. Motoda, T. Fawcett, R. Holte, P. Langley, P. Adriaans, Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem Solving, Machine Learning, Vol. 57, No. 1-2, pp.13-34, 2004. http://dx.doi.org/10.1023/B:MACH.0000035516.74817.51

R. Gajda, Utilizing Collaboration Theory to Evaluate Strategic Alliances, American Journal of Evaluation, Vol. 25, No. 1, pp. 65-77, 2004. http://dx.doi.org/10.1177/109821400402500105

M. Geringer, Strategic Determinants of Partner Selection Criteria in International Joint Ventures, Journal of International Business Studies, Vol. 22, No. 1, pp.755-786, 1991. http://dx.doi.org/10.1057/palgrave.jibs.8490291

M. Hitt, M. Dacin, E. Levitas, J. Arregle, A. Borza, Partner Selection in Emerging and Developed Market Contexts, Academy of Management Journal, Vol. 43, No. 3, pp. 440-467, 2000. http://dx.doi.org/10.2307/1556404

Z. Chen, Learning about Learners: System Learning in Virtual Learning Environment, International Journal of Computers, Communications and Control, 3(1):33-40, 2008. http://dx.doi.org/10.15837/ijccc.2008.1.2372

H. Grebla, C. Cenan, C., Distributed Machine Learning in a Medical Domain, International Journal of Computers, Communications & Control, 1(S):245-250, 2006.

Published

2010-03-01

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