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題名 運用文字探勘技術分析金融科技之發展與趨勢
Applying text mining techniques to the development and trends of fintech`s patent
作者 郝紹君
Hao, Shao Chun
貢獻者 洪為璽
Hung, Wei Hsi
郝紹君
Hao, Shao Chun
關鍵詞 文字探勘
金融科技
專利趨勢分析
機會探索
關鍵圖
Text mining
Fintech
Patent trend analysis
Chance discovery
KeyGraph
日期 2017
上傳時間 31-Jul-2017 11:32:31 (UTC+8)
摘要 現今科技日新月異，不斷突破創新，產業環境變動的步調也越來越快，新竄出之金融科技(Finance Technology)的應用，使得許多企業越加注重技術方面的研發創新，尤其，善加運用專利資訊能有效節省研發經費與時間。因此如何有效運用專利是企業維持競爭優勢不可或缺的一環。
有鑑於此，本研究搜集近年各國專利資料庫之專利資料，將資料分為三個時期，並區分申請中與已申請之專利資料，透過文字探勘技術與機會探索分析出金融科技之發展與趨勢，了解各時期詞彙間之關聯性與差異，再搭配視覺化工具KeyGraph，以描繪出金融科技領域之相關詞彙關聯趨勢圖，挖掘未來潛在趨勢。
本研究之結果了解金融科技在各時期的趨勢發展變化與尋求脈絡，以及過去各時期之專利佈局，因而從結果中發現金融科技之發展方向主體為支付領域，許多支付科技接連出現在三個時期中。然而近幾年，其他金融領域如投資、融資、保險、資料分析等也漸漸浮出，從本研究之第三個時期的高頻字詞高達34個可看出，可見金融科技之專利發展佈局已快速從支付領域拓展至其他金融領域。本研究所挖掘出之潛在趨勢顯示了未來金融科技領域中將會有五大重點發展領域，分別為服務整合領域之雲端科技、支付領域之生物辨識與穿戴支付與加密貨幣、資料分析領域之機器學習與人工智慧、信息收集與處理領域之遠程信息處理科技、以及理財投資領域之理財機器人。
期望本研究結果能幫助企業，在面臨新科技不斷衝擊產業，而產業不斷尋求創新發展之下，能夠快速檢閱目前市場趨勢，藉此釐清並改善自身之發展策略，以因應外部環境之變動，提供企業作為金融科技發展之策略參考，也能有助於企業釐清與制定金融科技之投資方向，以擁有持續的競爭優勢。
Nowadays, with the rapid advancement of information technologies, the changes of business environment and the way to deal with the changes are becoming faster and faster. The development and adoption of new financial technologies has made many enterprises pay more attention to the research and development (R&D) initiatives. Besides, making good use of patent information can effectively save the budget and time of R&D, so how to effectively use patent information is an indispensable part for enterprises to maintain their competitive advantages.
This study collected the patent data from the national patent database, and divided the data into three periods, and distinguished the data between the applying and the applied patents. Through the text mining techniques and chance discovery, this study explored the development and trends of financial technology and also aimed to understand the relevance and differences between the major terms in each period. Then, with the visual tool, KeyGraph, this study illustrated the associations between related terms, and proposed the potential future trends based on the graphs.
The results of this study help monitor the changes of the trends and financial technology’s development in the three periods, and understand the patent portfolios in each period. This study has found that the main direction of financial technology’s development is the payment field. Many technologies related to payment have successively appeared in the three periods. However, in recent years, other financial areas such as investment, financing, insurance, data analysis and other areas are gradually emerging, since we found 34 high-frequency terms in the third period. This also shows that the development of financial technology’s patent portfolios has expanded from payment to other financial areas. The potential trends of financial technology’s development in this study are five areas, namely, technologies of cloud, biometric and wearable payment and cryptocurrency, machine learning and artificial intelligence, telematics technology, and robo-advisors.
It is expected that this study can serve as a reference for the development of financial technology, and help enterprises be able to quickly review their current market trends, clarify and improve their own R&D strategies to respond to the changes in the external environment. Also, it is hoped that the results can help enterprises clarify and develop their own investment directions to maintain competitive advantages.
參考文獻 References
Altuntas, S., & Dereli, T. (2014). A novel approach based on DEMATEL method and
patent citation analysis for prioritizing a portfolio of investment projects. Expert Systems With Applications, 42(3), 1003-1012. http://dx.doi.org/10.1016/j.eswa.2014.09.018
Altuntas, S., Dereli, T., & Kusiak, A. (2015). Forecasting technology success based
on patent data. Technological Forecasting And Social Change, 96, 202-214. http://dx.doi.org/10.1016/j.techfore.2015.03.011
Ark Speier, Brain Hughes,Dennis Fortnum, Ian Pollari, Warren Mead. (2017). The
Pulse of Fintech, Q3 2016, KPMG, Retrieved 19 June 2017.
Arner, D. W., Barberis, J. N., & Buckley, R. P. (2015). The Evolution of Fintech: A
New Post-Crisis Paradigm? (2015/047). Hong Kong.
Ashton, W. B., and Sen, R. K. (1988), “Using Patent Information in Technology Business Planning-II”, Research Technology Management, November-December, 31(6), 42-46.
Boldrini, L., & Giorgino, M. (2017). An explorative study on Robo advisory and digital trends in the asset management industry. POLITESI. Retrieved 23 June 2017, from https://www.politesi.polimi.it/handle/10589/133337
Chen, L. C., Yu, T. J., & Hsieh, C. J. (2013). KeyGraph-based chance discovery for exploring the development of e-commerce topics. Scientometrics, 95 (1), 257-275.
Daim, T., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting And Social Change, 73(8), 981-1012. http://dx.doi.org/10.1016/j.techfore.2006.04.004
Dhar, Vasant and Stein, Roger M., (December 14, 2016). FinTech Platforms and Strategy, MIT Sloan Research Paper No. 5183-16. Available at SSRN: https://ssrn.com/abstract=2892098
Fayyad, U. M., Pitatesky-Shapiro, G., Smyth, P., & Uthurasamy, R. (1996). Advances in knowledge discovery and data mining, AAAI/MIT Press.
Feldman, R., & Dagan, I. (1995, August). Knowledge Discovery in Textual Databases (KDT). In KDD (pp.112-117). Canada.
Groshoff, David and Urien, Kurtis R. and Nguyen, Alex, (July 31, 2014).
Crowdfunding 6.0: Does the SEC`s FinTech Law Failure Reveal the Agency’s True Mission to Protect — Solely Accredited — Investors? Available at SSRN: https://ssrn.com/abstract=2483371 or http://dx.doi.org/10.2139/ssrn.2483371
Hall, Browyn H., Griluchesm Zvi, Hausman, Jerry A. (1986), “Patents and R and D: Is There A Lag?”, International Economic Review, 27(2), 265-283.
Hauke, S., & Leker, J. (2016). Using Startup Communication for Opportunity Recognition — an approach to identift future products trends. World Scientific Publishing Company.
Hong, C. F. (2009). Qualitative chance discovery–Extracting competitive advantages. Information Sciences, 179 (11), 1570-1583.
Hotho, A., Nuernberger, A., & Paaß, G. (2005). A Brief Survey of Text Mining. Journal for Language Technology and Computational Lingustics, from https://www.kde.cs.uni-kassel.de/hotho/pub/2005/hotho05TextMining.pdf
Julian Skan, Richard Lumb, Samad Masood, Sean K. Conway. (2017). The Boom in
Global Fintech Investment, Accenture, Retrieved 17 June 2017.
Wu J., K. Ota, M. Dong and C. Li, (2016), "A Hierarchical Security Framework for
Defending Against Sophisticated Attacks on Wireless Sensor Networks in Smart Cities," In IEEE Access, vol. 4, no., pp. 416-424.
doi: 10.1109/ACCESS.2016.2517321
Kim, G., & Shim, M. (2016). Study on the Mobile FinTech Vacant Technology using Patent Analysis. 학술교육원 eArticle.
Kim, J., Hwang, M., Jeong, D., & Jung, H. (2012). Technology trends analysis and forecasting application based on decision tree and statistical feature analysis. Expert Systems With Applications, 39(16), 12618-12625. http://dx.doi.org/10.1016/j.eswa.2012.05.021
Koo, J. M., & Cho, S. B. (2005). Interpreting chance for computer security by viterbi algorithm with edit distance. New Mathematics and Natural Computation, 1(3), 421-433.
Lee, C., Jeon, J., & Park, Y. (2011). Monitoring trends of technological changes based on the dynamic patent lattice: A modified formal concept analysis approach. Technological Forecasting And Social Change, 78(4), 690-702. http://dx.doi.org/10.1016/j.techfore.2010.11.010
Lee, S., Kim, M., Park, Y., & Kim, C. (2016). Identification of a technological chance
in product-service system using KeyGraph and text mining on business method patents: International Journal of Technology Management: Vol 70, No 4. Inderscienceonline.com. Retrieved 24 June 2017, from http://www.inderscienceonline.com/doi/pdf/10.1504/IJTM.2016.075884
Lee, S., Yoon, B., Lee, C., & Park, J. (2009). Business planning based on
technological capabilities: Patent analysis for technology-driven roadmapping,
Technological Forecasting And Social Change, 76(6), 769-786.
http://dx.doi.org/10.1016/j.techfore.2009.01.003
Li, G., Dai, J.S., Park, EM. et al. J Comput Virol Hack Tech (2017). A study on the
service and trend of Fintech security based on text-mining: focused on the data of Korean online news. doi:10.1007/s11416-016-0288-9
Lin, Tom C. W., Infinite Financial Intermediation (January 5, 2016). Wake Forest
Law Review, Vol. 50, No. 643, 2015; Temple University Legal Studies Research Paper No. 2016-06.
LI, S., & WONG, K. (2016). Educational data mining using chance discovery from discussion board. Repository.lib.ied.edu.hk. Retrieved 24 June 2017, from http://repository.lib.ied.edu.hk/jspui/handle/2260.2/20402
Manning, C. D. (1999). Foundations of statistical natural language processing. MA: MIT press.
Mogee, M. E. (1991), “Using Patent Data For Technology Analysis And Planning”, Research Technology Management, 34(4), 43.
M. Y. Day and C. C. Lee, "Deep learning for financial sentiment analysis on finance
news providers," 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), San Francisco, CA, 2016, pp. 1127-1134.
Ohsawa, Y., Benson, N. E., & Yachida, M. (1998, April). KeyGraph: Automatic indexing by co-occurrence graph based on building construction metaphor. In Proceedings of the IEEE International Forum on Research and Technology Advances in Digital Libraries. (pp. 12-18).
Ohsawa, Y. (2002a). Chance discoveries for making decisions in complex real world. New Generation Computing, 20(2), 143-163.
Ohsawa, Y. (2002b). KeyGraph as Risk Explorer in Earthquake–Sequence. Journal of Contingencies and Crisis Management, 10(3), 119-128.
Ohsawa, Y., & Fukuda, H. (2002). Chance discovery by stimulated groups of people. Application to understanding consumption of rare food. Journal of Contingencies and Crisis Management, 10(3), 129-138.
Ohsawa, Y., & McBurney, P. (2003). Chance discovery. Heidelberg: Springer.
Schueffel, Patrick (2016-03-09). "Taming the Beast: A Scientific Definition of
Fintech". Journal of Innovation Management. 4 (4): 32–54. ISSN 2183-0606
Seo, JH. & Park, EM. Wireless Pers Commun (2017). A Study on Financing Security
for Smartphones Using Text Mining. doi:10.1007/s11277-017-4121-7
Seongyong Choi & Sunghae Jun (2014) Vacant technology forecasting using
new Bayesian patent clustering, Technology Analysis & Strategic Management, 26:3, 241-251, DOI:10.1080/09537325.2013.850477
Sun PC., Hong CF., Kuo TF., Iqbal R. (2017) Chance Discovery in a Group-Trading
Model ─ Creating an Innovative Tour Package with Freshwater Fish Farms at Yilan. Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science, vol 10192. Springer, Cham
Tsai, Y., Huang, Y., & Yang, J. (2016). Strategies for the development of offshore
wind technology for far-east countries – A point of view from patent analysis. Renewable And Sustainable Energy Reviews, 60, 182-194. http://dx.doi.org/10.1016/j.rser.2016.01.102
Yoon, B., & Park, Y. (2003). A text-mining-based patent network: Analytical tool for
high-technology trend, ScienceDirect. Sciencedirect.com. Retrieved 7 July 2017, from http://www.sciencedirect.com/science/article/pii/S1047831003000439
Wang, H., & Ohsawa, Y. (2012). Idea discovery: A scenario-based systematic approach for decision making in market innovation. Expert Systems with Applications, 40(2), 429-438.
Walch, Angela, (March 16, 2015). The Bitcoin Blockchain as Financial Market
Infrastructure: A Consideration of Operational Risk. 18 NYU Journal of Legislation and Public Policy 837 (2015). Available at SSRN: https://ssrn.com/abstract=2579482
Wu, M., Chang, K., Zhou, W., Hao, J., Yuan, C., & Chang, K. (2015). Patent
Deployment Strategies and Patent Value in LED Industry. PLOS ONE, 10(6), e0129911. http://dx.doi.org/10.1371/journal.pone.0129911
描述 碩士
國立政治大學
企業管理研究所(MBA學位學程)
104363041
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0104363041
資料類型 thesis
dc.contributor.advisor 洪為璽zh_TW
dc.contributor.advisor Hung, Wei Hsien_US
dc.contributor.author (Authors) 郝紹君zh_TW
dc.contributor.author (Authors) Hao, Shao Chunen_US
dc.creator (作者) 郝紹君zh_TW
dc.creator (作者) Hao, Shao Chunen_US
dc.date (日期) 2017en_US
dc.date.accessioned 31-Jul-2017 11:32:31 (UTC+8)-
dc.date.available 31-Jul-2017 11:32:31 (UTC+8)-
dc.date.issued (上傳時間) 31-Jul-2017 11:32:31 (UTC+8)-
dc.identifier (Other Identifiers) G0104363041en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/111579-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 企業管理研究所(MBA學位學程)zh_TW
dc.description (描述) 104363041zh_TW
dc.description.abstract (摘要) 現今科技日新月異，不斷突破創新，產業環境變動的步調也越來越快，新竄出之金融科技(Finance Technology)的應用，使得許多企業越加注重技術方面的研發創新，尤其，善加運用專利資訊能有效節省研發經費與時間。因此如何有效運用專利是企業維持競爭優勢不可或缺的一環。
有鑑於此，本研究搜集近年各國專利資料庫之專利資料，將資料分為三個時期，並區分申請中與已申請之專利資料，透過文字探勘技術與機會探索分析出金融科技之發展與趨勢，了解各時期詞彙間之關聯性與差異，再搭配視覺化工具KeyGraph，以描繪出金融科技領域之相關詞彙關聯趨勢圖，挖掘未來潛在趨勢。
本研究之結果了解金融科技在各時期的趨勢發展變化與尋求脈絡，以及過去各時期之專利佈局，因而從結果中發現金融科技之發展方向主體為支付領域，許多支付科技接連出現在三個時期中。然而近幾年，其他金融領域如投資、融資、保險、資料分析等也漸漸浮出，從本研究之第三個時期的高頻字詞高達34個可看出，可見金融科技之專利發展佈局已快速從支付領域拓展至其他金融領域。本研究所挖掘出之潛在趨勢顯示了未來金融科技領域中將會有五大重點發展領域，分別為服務整合領域之雲端科技、支付領域之生物辨識與穿戴支付與加密貨幣、資料分析領域之機器學習與人工智慧、信息收集與處理領域之遠程信息處理科技、以及理財投資領域之理財機器人。
期望本研究結果能幫助企業，在面臨新科技不斷衝擊產業，而產業不斷尋求創新發展之下，能夠快速檢閱目前市場趨勢，藉此釐清並改善自身之發展策略，以因應外部環境之變動，提供企業作為金融科技發展之策略參考，也能有助於企業釐清與制定金融科技之投資方向，以擁有持續的競爭優勢。		zh_TW
dc.description.abstract (摘要) Nowadays, with the rapid advancement of information technologies, the changes of business environment and the way to deal with the changes are becoming faster and faster. The development and adoption of new financial technologies has made many enterprises pay more attention to the research and development (R&D) initiatives. Besides, making good use of patent information can effectively save the budget and time of R&D, so how to effectively use patent information is an indispensable part for enterprises to maintain their competitive advantages.
This study collected the patent data from the national patent database, and divided the data into three periods, and distinguished the data between the applying and the applied patents. Through the text mining techniques and chance discovery, this study explored the development and trends of financial technology and also aimed to understand the relevance and differences between the major terms in each period. Then, with the visual tool, KeyGraph, this study illustrated the associations between related terms, and proposed the potential future trends based on the graphs.
The results of this study help monitor the changes of the trends and financial technology’s development in the three periods, and understand the patent portfolios in each period. This study has found that the main direction of financial technology’s development is the payment field. Many technologies related to payment have successively appeared in the three periods. However, in recent years, other financial areas such as investment, financing, insurance, data analysis and other areas are gradually emerging, since we found 34 high-frequency terms in the third period. This also shows that the development of financial technology’s patent portfolios has expanded from payment to other financial areas. The potential trends of financial technology’s development in this study are five areas, namely, technologies of cloud, biometric and wearable payment and cryptocurrency, machine learning and artificial intelligence, telematics technology, and robo-advisors.
It is expected that this study can serve as a reference for the development of financial technology, and help enterprises be able to quickly review their current market trends, clarify and improve their own R&D strategies to respond to the changes in the external environment. Also, it is hoped that the results can help enterprises clarify and develop their own investment directions to maintain competitive advantages.		en_US
dc.description.tableofcontents CHAPTER 1 INTRODUCTION 9
1.1 BACKGROUND INFORMATION AND RESEARCH MOTIVATION 9
1.2 PURPOSES AND QUESTIONS 13
1.3 THESIS OUTLINE 14
CHAPTER 2 LITERATURE REVIEW 16
2.1 OVERVIEW OF FINTECH 16
2.1.1 Definition 16
2.1.2 Fintech Trend Analysis 21
2.2 PATENT ANALYSIS FOR TRENDS 23
2.2.1 Value of Patent Information and Analysis 23
2.2.2 Patent Trend Analysis 27
2.3 TEXT MINING 28
2.3.1 Definition and Related Research Areas 29
2.3.2 Text-Mining-Based Patent Trend Analysis 31
2.4 CHANCE DISCOVERY 32
2.4.1 Definition 32
2.4.2 Related Works of Chance Discovery 33
CHAPTER 3 RESEARCH METHODOLOGY 35
3.1 RESEARCH METHOD 35
3.1.1 Overview 35
3.1.2 N-gram 35
3.1.3 KeyGraph 36
3.2 RESEARCH PROCESS 43
CHAPTER 4 RESULTS AND DISCUSSIONS 47
4.1 STATUS OF FINAL DATA SET 47
4.1.1 Data Collection and Data Cleansing 47
4.1.2 Data Integration and Clustering 49
4.2 DATA ANALYSIS 50
4.2.1 Step One: Word Preprocess 50
4.2.2 High-frequent Terms Extraction 51
4.2.3 Links Extraction 53
4.2.4 Key Terms Extraction 53
4.2.5 Key Links Extraction 55
4.2.6 Key terms Extraction 55
4.3 KEYGRAPH OF THE PERIODS 56
4.3.1 1998 to 2004 Terms’ Association Graph 56
4.3.2 2005 to 2010 Terms’ Association Graph 63
4.3.3 2011 to 2017 Terms’ Association Graph 70
4.4 CROSS COMPARISON AND DISCUSSIONS 79
CHAPTER 5 CONCLUSION AND SUGGESTIONS 84
5.1 CONCLUSION 84
5.2 RESEARCH CONTRIBUTIONS AND LIMITATIONS 88
5.3 FUTURE RESEARCH 91
REFERENCES 93		zh_TW
dc.format.extent 5997995 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0104363041en_US
dc.subject (關鍵詞) 文字探勘zh_TW
dc.subject (關鍵詞) 金融科技zh_TW
dc.subject (關鍵詞) 專利趨勢分析zh_TW
dc.subject (關鍵詞) 機會探索zh_TW
dc.subject (關鍵詞) 關鍵圖zh_TW
dc.subject (關鍵詞) Text miningen_US
dc.subject (關鍵詞) Fintechen_US
dc.subject (關鍵詞) Patent trend analysisen_US
dc.subject (關鍵詞) Chance discoveryen_US
dc.subject (關鍵詞) KeyGraphen_US
dc.title (題名) 運用文字探勘技術分析金融科技之發展與趨勢zh_TW
dc.title (題名) Applying text mining techniques to the development and trends of fintech`s patenten_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) References
Altuntas, S., & Dereli, T. (2014). A novel approach based on DEMATEL method and
patent citation analysis for prioritizing a portfolio of investment projects. Expert Systems With Applications, 42(3), 1003-1012. http://dx.doi.org/10.1016/j.eswa.2014.09.018
Altuntas, S., Dereli, T., & Kusiak, A. (2015). Forecasting technology success based
on patent data. Technological Forecasting And Social Change, 96, 202-214. http://dx.doi.org/10.1016/j.techfore.2015.03.011
Ark Speier, Brain Hughes,Dennis Fortnum, Ian Pollari, Warren Mead. (2017). The
Pulse of Fintech, Q3 2016, KPMG, Retrieved 19 June 2017.
Arner, D. W., Barberis, J. N., & Buckley, R. P. (2015). The Evolution of Fintech: A
New Post-Crisis Paradigm? (2015/047). Hong Kong.
Ashton, W. B., and Sen, R. K. (1988), “Using Patent Information in Technology Business Planning-II”, Research Technology Management, November-December, 31(6), 42-46.
Boldrini, L., & Giorgino, M. (2017). An explorative study on Robo advisory and digital trends in the asset management industry. POLITESI. Retrieved 23 June 2017, from https://www.politesi.polimi.it/handle/10589/133337
Chen, L. C., Yu, T. J., & Hsieh, C. J. (2013). KeyGraph-based chance discovery for exploring the development of e-commerce topics. Scientometrics, 95 (1), 257-275.
Daim, T., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting And Social Change, 73(8), 981-1012. http://dx.doi.org/10.1016/j.techfore.2006.04.004
Dhar, Vasant and Stein, Roger M., (December 14, 2016). FinTech Platforms and Strategy, MIT Sloan Research Paper No. 5183-16. Available at SSRN: https://ssrn.com/abstract=2892098
Fayyad, U. M., Pitatesky-Shapiro, G., Smyth, P., & Uthurasamy, R. (1996). Advances in knowledge discovery and data mining, AAAI/MIT Press.
Feldman, R., & Dagan, I. (1995, August). Knowledge Discovery in Textual Databases (KDT). In KDD (pp.112-117). Canada.
Groshoff, David and Urien, Kurtis R. and Nguyen, Alex, (July 31, 2014).
Crowdfunding 6.0: Does the SEC`s FinTech Law Failure Reveal the Agency’s True Mission to Protect — Solely Accredited — Investors? Available at SSRN: https://ssrn.com/abstract=2483371 or http://dx.doi.org/10.2139/ssrn.2483371
Hall, Browyn H., Griluchesm Zvi, Hausman, Jerry A. (1986), “Patents and R and D: Is There A Lag?”, International Economic Review, 27(2), 265-283.
Hauke, S., & Leker, J. (2016). Using Startup Communication for Opportunity Recognition — an approach to identift future products trends. World Scientific Publishing Company.
Hong, C. F. (2009). Qualitative chance discovery–Extracting competitive advantages. Information Sciences, 179 (11), 1570-1583.
Hotho, A., Nuernberger, A., & Paaß, G. (2005). A Brief Survey of Text Mining. Journal for Language Technology and Computational Lingustics, from https://www.kde.cs.uni-kassel.de/hotho/pub/2005/hotho05TextMining.pdf
Julian Skan, Richard Lumb, Samad Masood, Sean K. Conway. (2017). The Boom in
Global Fintech Investment, Accenture, Retrieved 17 June 2017.
Wu J., K. Ota, M. Dong and C. Li, (2016), "A Hierarchical Security Framework for
Defending Against Sophisticated Attacks on Wireless Sensor Networks in Smart Cities," In IEEE Access, vol. 4, no., pp. 416-424.
doi: 10.1109/ACCESS.2016.2517321
Kim, G., & Shim, M. (2016). Study on the Mobile FinTech Vacant Technology using Patent Analysis. 학술교육원 eArticle.
Kim, J., Hwang, M., Jeong, D., & Jung, H. (2012). Technology trends analysis and forecasting application based on decision tree and statistical feature analysis. Expert Systems With Applications, 39(16), 12618-12625. http://dx.doi.org/10.1016/j.eswa.2012.05.021
Koo, J. M., & Cho, S. B. (2005). Interpreting chance for computer security by viterbi algorithm with edit distance. New Mathematics and Natural Computation, 1(3), 421-433.
Lee, C., Jeon, J., & Park, Y. (2011). Monitoring trends of technological changes based on the dynamic patent lattice: A modified formal concept analysis approach. Technological Forecasting And Social Change, 78(4), 690-702. http://dx.doi.org/10.1016/j.techfore.2010.11.010
Lee, S., Kim, M., Park, Y., & Kim, C. (2016). Identification of a technological chance
in product-service system using KeyGraph and text mining on business method patents: International Journal of Technology Management: Vol 70, No 4. Inderscienceonline.com. Retrieved 24 June 2017, from http://www.inderscienceonline.com/doi/pdf/10.1504/IJTM.2016.075884
Lee, S., Yoon, B., Lee, C., & Park, J. (2009). Business planning based on
technological capabilities: Patent analysis for technology-driven roadmapping,
Technological Forecasting And Social Change, 76(6), 769-786.
http://dx.doi.org/10.1016/j.techfore.2009.01.003
Li, G., Dai, J.S., Park, EM. et al. J Comput Virol Hack Tech (2017). A study on the
service and trend of Fintech security based on text-mining: focused on the data of Korean online news. doi:10.1007/s11416-016-0288-9
Lin, Tom C. W., Infinite Financial Intermediation (January 5, 2016). Wake Forest
Law Review, Vol. 50, No. 643, 2015; Temple University Legal Studies Research Paper No. 2016-06.
LI, S., & WONG, K. (2016). Educational data mining using chance discovery from discussion board. Repository.lib.ied.edu.hk. Retrieved 24 June 2017, from http://repository.lib.ied.edu.hk/jspui/handle/2260.2/20402
Manning, C. D. (1999). Foundations of statistical natural language processing. MA: MIT press.
Mogee, M. E. (1991), “Using Patent Data For Technology Analysis And Planning”, Research Technology Management, 34(4), 43.
M. Y. Day and C. C. Lee, "Deep learning for financial sentiment analysis on finance
news providers," 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), San Francisco, CA, 2016, pp. 1127-1134.
Ohsawa, Y., Benson, N. E., & Yachida, M. (1998, April). KeyGraph: Automatic indexing by co-occurrence graph based on building construction metaphor. In Proceedings of the IEEE International Forum on Research and Technology Advances in Digital Libraries. (pp. 12-18).
Ohsawa, Y. (2002a). Chance discoveries for making decisions in complex real world. New Generation Computing, 20(2), 143-163.
Ohsawa, Y. (2002b). KeyGraph as Risk Explorer in Earthquake–Sequence. Journal of Contingencies and Crisis Management, 10(3), 119-128.
Ohsawa, Y., & Fukuda, H. (2002). Chance discovery by stimulated groups of people. Application to understanding consumption of rare food. Journal of Contingencies and Crisis Management, 10(3), 129-138.
Ohsawa, Y., & McBurney, P. (2003). Chance discovery. Heidelberg: Springer.
Schueffel, Patrick (2016-03-09). "Taming the Beast: A Scientific Definition of
Fintech". Journal of Innovation Management. 4 (4): 32–54. ISSN 2183-0606
Seo, JH. & Park, EM. Wireless Pers Commun (2017). A Study on Financing Security
for Smartphones Using Text Mining. doi:10.1007/s11277-017-4121-7
Seongyong Choi & Sunghae Jun (2014) Vacant technology forecasting using
new Bayesian patent clustering, Technology Analysis & Strategic Management, 26:3, 241-251, DOI:10.1080/09537325.2013.850477
Sun PC., Hong CF., Kuo TF., Iqbal R. (2017) Chance Discovery in a Group-Trading
Model ─ Creating an Innovative Tour Package with Freshwater Fish Farms at Yilan. Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science, vol 10192. Springer, Cham
Tsai, Y., Huang, Y., & Yang, J. (2016). Strategies for the development of offshore
wind technology for far-east countries – A point of view from patent analysis. Renewable And Sustainable Energy Reviews, 60, 182-194. http://dx.doi.org/10.1016/j.rser.2016.01.102
Yoon, B., & Park, Y. (2003). A text-mining-based patent network: Analytical tool for
high-technology trend, ScienceDirect. Sciencedirect.com. Retrieved 7 July 2017, from http://www.sciencedirect.com/science/article/pii/S1047831003000439
Wang, H., & Ohsawa, Y. (2012). Idea discovery: A scenario-based systematic approach for decision making in market innovation. Expert Systems with Applications, 40(2), 429-438.
Walch, Angela, (March 16, 2015). The Bitcoin Blockchain as Financial Market
Infrastructure: A Consideration of Operational Risk. 18 NYU Journal of Legislation and Public Policy 837 (2015). Available at SSRN: https://ssrn.com/abstract=2579482
Wu, M., Chang, K., Zhou, W., Hao, J., Yuan, C., & Chang, K. (2015). Patent
Deployment Strategies and Patent Value in LED Industry. PLOS ONE, 10(6), e0129911. http://dx.doi.org/10.1371/journal.pone.0129911		zh_TW