Advanced SearchSearch Tips
A Technology Analysis Model using Dynamic Time Warping
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
A Technology Analysis Model using Dynamic Time Warping
Choi, JunHyeog; Jun, SungHae;
  PDF(new window)
Technology analysis is to analyze technological data such as patent and paper for a given technology field. From the results of technology analysis, we can get novel knowledge for R&D planing and management. For the technology analysis, we can use diverse methods of statistics. Time series analysis is one of efficient approaches for technology analysis, because most technologies have researched and developed depended on time. So many technological data are time series. Time series data are occurred through time. In this paper, we propose a methodology of technology forecasting using the dynamic time warping (DTW) of time series analysis. To illustrate how to apply our methodology to real problem, we perform a case study of patent documents in target technology field. This research will contribute to R&D planning and technology management.
Patent big data;Time series clustering;Dynamic time warping;Management;of technology;
 Cited by
Bayesian Regression Modeling for Patent Keyword Analysis,;;

한국컴퓨터정보학회논문지, 2016. vol.21. 1, pp.125-129 crossref(new window)
Big Data Smoothing and Outlier Removal for Patent Big Data Analysis,;;

한국컴퓨터정보학회논문지, 2016. vol.21. 8, pp.77-84 crossref(new window)
Statistical Technology Analysis for Competitive Sustainability of Three Dimensional Printing, Sustainability, 2017, 9, 7, 1142  crossref(new windwow)
Roper, A. T., Cunningham, S. W., Porter, A. L., Mason, T. W., Rossini F. A., Banks J. Forecasting and Management of Technology, John Wiley & Sons, 2011.

Cyert, R. M., Kumar, P., "Technology Management and the Future", IEEE Transactions on Engineering Management, Vol. 41, No. 4, pp. 333-334, 1994. crossref(new window)

McDermott, C. M., Kang, H., Walsh, S., "A Framework for Technology Management in Services", IEEE Transactions on Engineering Management, Vol. 48, No. 3, pp. 333-341, 2001. crossref(new window)

Jun, S., Park, S., Jang, D. "Technology Forecasting using Matrix Map and Patent Clustering", Industrial Management & Data Systems, Vol. 112, Iss. 5, pp. 786-807, 2012. crossref(new window)

Martino, J. P., "Technology forecasting-An overview", Management Science, Vol. 26, No. 1, pp. 28-33, 1980. crossref(new window)

Yun, Y. C., Jeong, G. H., Kim, S. H., "A Delphi technology forecasting approach using a semi-Markov concept", Technological Forecasting and Social Change, Vol. 40, pp. 273-287, 1991. crossref(new window)

Hunt, D., Nguyen, L., Rodgers, M., Patent Searching Tools & Techniques, Wiley, 2007.

Keogh, E. J., Pazzani, M. J. "Derivative dynamic time warping," Proceedings of the 1st SIAM International Conference on Data Mining, pp. 1-11, 2001.

J. Choi, S. Jun, "Time Series Clustering for Patent Big Data", Proceedings of Asia Workshop on Convergence Information Technology of KSCI 2014, pp. 159-162.

Muller, M., Information Retrieval for Music and Motion, Springer, pp. 69-84, 2007.

Daim, T. U., Rueda, G., Martin, H., Gerdsri, P. "Forecasting emerging technologies: Use of bibliometrics and patent analysis", Technological Forecasting and Social Change, Vol. 73, Iss. 8, pp. 981-1012, 2006. crossref(new window)

Jun, S. "IPC Code Analysis of Patent Documents Using Association Rules and Maps-Patent Analysis of Database Technology", Communications in Computer and Information Science, Vol. 258, pp. 21-30, 2011. crossref(new window)

Hwang, J., Kim, B. "Analysis on the multitechnology capabilities of Korea and Taiwan using patent bibliometrics," Asian Journal of Technology Innovation, Vol. 14, No. 2, pp. 183- 199, 2006. crossref(new window)

Jun, S., Park, S., Jang, D., "Technology Forecasting using Matrix Map and Patent Clustering", Industrial Management & Data Systems, Vol. 112, Iss. 5, pp. 786-807, 2012. crossref(new window)

Mishra, S., Deshmukh, S. G., Vrat, P., "Matching of technological forecasting technique to a technology", Technological Forecasting and Social Change, Vol. 69, pp. 1-27, 2002. crossref(new window)

Zhang, X., Liu, J., Du, Y., Lv, T., "A Novel Clustering method on Time Series Data," Expert Systems with Application, Vol. 38, pp. 11891-11900, 2011. crossref(new window)

Rabiner, L. R., Juang, B. H., Fundamentals of Speech Recognition, Prentice Hall Signal Processing Series, 1993.

Zhao, Y., R and Data Mining - Examples and Case Studies, Academic Press, Elsevier, 2013.

Liao, T. W., "A Clustering Procedure for Exploratory Mining of Vector Time Series," Pattern Recognition, Vol. 40, pp. 2550-2562, 2007. crossref(new window)

WIPSON, WIPS Corporation,, 2014.

Lee, S., Jun, S., "Key IPC Codes Extraction Using Classification and Regression Tree Structure," Advances in Intelligent Systems and ComputingVolume 271, pp 101-109, 2014. crossref(new window)

R Development Core Team, R: A language and environment for statistical computing, R Foundation for Statistical Computing,, 2014.

Giorgino, T., Package 'dtw' - Dynamic time warping algorithms, R CRAN, 2014.

WIPO IPC, International Patent Classification(IPC), World Intellectual Property Organization,, 2014.

WIPO, World Intellectual Property Organization,, 2014