研究生: |
劉哲瑋 Che-wei Liu |
---|---|
論文名稱: |
利用量化及質性方法監測分析Web 2.0商業生態系統 Monitoring Web 2.0 Business Ecosystem Quantitatively and Qualitatively |
指導教授: |
林福仁
Fu-ren Lin |
口試委員: | |
學位類別: |
碩士 Master |
系所名稱: |
科技管理學院 - 科技管理研究所 Institute of Technology Management |
論文出版年: | 2007 |
畢業學年度: | 95 |
語文別: | 中文 |
論文頁數: | 86 |
中文關鍵詞: | Web 2.0 、商業生態系統 、科技監測 、關係擷取 、事件偵測 |
外文關鍵詞: | Web 2.0, Business Ecosystem, Technology Monitoring, Relation Extraction, Event Detection |
相關次數: | 點閱:2 下載:0 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
Web 2.0在過去三年間經歷了快速成長的階段,同時之間也逐漸的形成一個複雜的商業生態系統。 在商業生態系統中,每家公司會選擇使用合作或競爭的方式來搶奪生態系中經濟資源,這些行為將會直接或間接的影響到整個生態系的發展,也因此使得各公司在這個動態的生態系統中處於一個不穩定的狀態中,而公司在生態系統中的角色也會隨時間而有所變化。
本論文提出了兩個監測系統來分析網路流量、新聞報導及相關的部落格文章,藉此來觀察這個複雜且仍在自我成長中的Web 2.0商業生態系統。第一個系統將著重在公司間關係的建置,以此出發來辨別生態系統中的「基石角色」(Keystone player)及「利基角色」(Niche player)。另一個系統則是利用部落格及流量的變化來觀察系統中每位成員所發生的特殊事件。
本研究所發展的系統稱作Web eco-Watcher將會協助專家找出「基石角色」、「利基角色」及偵測特殊事件。結合生態系統的演進資料、「基石角色」所併購的公司資料、相關的新聞報導及專家本身的背景知識可以協助建立「基石角色」所使用的併購策略;而過濾不顯著的事件將可減低專家在觀察商業生態系統中的公司時所耗費的時間成本。
During the past three years, the Web 2.0 comes through a rapid growing period, and gradually forms a complicated business ecosystem. Each company collaborates or competes in economic web of relationships directly or indirectly would eventually affect the ecosystem, so the relationships under the ecosystem don’t always keep in a stable condition in the dynamic growing environment. Therefore, the players will change its role during different period of time.
This thesis proposes a monitoring system called Web eco-Watcher to monitor the complicated and self-evolving business ecosystem of Web 2.0 using the observed Web site traffic, news and blog articles. Qualitatively, it extracts the relationships to identify keystone and niche players among business ecosystem of Web 2.0, and quantitatively, it detects special events for each player via the variations of frequency of discussing observed Web site in blogs and its traffic.
The applications of Web eco-Watcher help experts identify the keystone and niche players and detect special events for each player. The composition of knowledge from the evolution of the ecosystem and the news reports on observed companies with background information will specify the acquisition strategy each keystone player exercises. To filter out information on insignificant events will also help reduce the cost on monitoring the players within the ecosystem.
ACE (Automatic Content Extraction) English Annotation Guidelines for Entities Version 5.6.6 [Electronic (2006). Version]. Retrieved December 23, 2006 from http://projects.ldc.upenn.edu/ace/docs/English-Entities-Guidelines_v5.6.6.pdf.
ACE (Automatic Content Extraction) English Annotation Guidelines for Relations Version 5.8.3 [Electronic (2005). Version]. Retrieved December 23, 2006 from http://projects.ldc.upenn.edu/ace/docs/English-Relations-Guidelines_v5.8.3.pdf.
Alahakoon, D., Halgamuge, S. K., & Srinivasan, B. (1998). A self-growing cluster development approach to data mining. Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on, 3.
Allan, J. (2002). Introduction to topic detection and tracking. The Kluwer International Series On Information Retrieval, 1-16.
Appelt, D. E., & Israel, D. (1999). Introduction to Information Extraction Technology. A tutorial prepared for IJCAI-99. Artificial Intelligence Center, SRI International.
Coates, J. F., & Coates, I. J. F. (1986). Issues Management: How You Can Plan, Organize, and Manage for the Future: Lomond.
Cunningham, H. (2002). GATE, a General Architecture for Text Engineering. Computers and the Humanities, 36(2), 223-254.
Havre, S., Hetzler, B., & Nowell, L. (2000). ThemeRiver: Visualizing Theme Changes over Time. Proc. IEEE Symposium on Information Visualization, 115-123.
Heer, J., Card, S. K., & Landay, J. A. (2005). prefuse: a toolkit for interactive information visualization. Conference on Human Factors in Computing Systems, 421-430.
Hepple, M. (2000). Independence and commitment: assumptions for rapid training and execution of rule-based POS taggers. Proceedings of the 38th Annual Meeting on Association for Computational Linguistics, 278-277.
Iansiti, M., & Levien, R. (2004). Strategy as Ecology. Harvard Business Review, 82(3), 68-78.
Klein, D., & Manning, C. D. (2003a). Accurate unlexicalized parsing. Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics, 423-430.
Klein, D., & Manning, C. D. (2003b). Fast exact inference with a factored model for natural language parsing. Advances in Neural Information Processing Systems, 15, 3-10.
Kongthon, A. (2004). A Text Mining Framework for Discovering Technological Intelligence to Support Science and Technology Management. Georgia Institute of Technology.
Kontostathis, A., Galitsky, L., Pottenger, W. M., Roy, S., & Phelps, D. J. (2003). A survey of emerging trend detection in textual data mining. Survey of Text Mining, 185-224.
Losiewicz, P., Oard, D., & Kostoff, R. (2000). Text Data Mining to Support Science and technology management. Journal of Intelligent Information Systems, 15(2), 99-119.
Maynard, D., Yankova, M., Kourakis, A., & Kokossis, A. (2005). Ontology-based information extraction for market monitoring and technology watch.
Mei, Q., & Zhai, C. X. (2005). Discovering evolutionary theme patterns from text: an exploration of temporal text mining. Conference on Knowledge Discovery in Data, 198-207.
Naisbett, J., & Aburdene, P. (1990). Megatrends 2000. Sedgwick and Jackson, London.
Porter, A. L., & Detampel, M. J. (1995). Technology opportunities analysis. Technological Forecasting and Social Change, 49(3), 237-255.
Solorio, T. (2004). Improvement of Named Entity Tagging by Machine Learning. Technical Report CCC-04-004, Coordinacion de Ciencias Computacionales, INAOE, Mexico.
Swan, R., & Jensen, D. (2000). TimeMines: Constructing Timelines with Statistical Models of Word Usage. KDD-2000 Workshop on Text Mining.
Teichert, T., & Mittermayer, M. A. (2002). Text mining for technology monitoring. Engineering Management Conference, 2002. IEMC'02. 2002 IEEE International, 2.
Turmo, J., Ageno, A., & Catala, N. (2006). Adaptive information extraction. ACM Computing Surveys (CSUR), 38(2).
Udechukwu, A., Barker, K., & Alhajj, R. (2004). Discovering all frequent trends in time series. Proceedings of the winter international synposium on Information and communication technologies, 1-6.
Wang, T., Li, Y., Bontcheva, K., Cunningham, H., & Wang, J. (2006). Automatic Extraction of Hierarchical Relations from Text. Proceedings of the Third European Semantic Web Conference (ESWC 2006), 215-229.
Yoon, B. U., Yoon, C. B., & Park, Y. T. (2002). On the development and application of a selforganizing feature mapbased patent map. R&D Management, 32(4), 291-300.
Yuan, J., & Zhu, D. (2004). A Study on Technology Monitoring Based on Text Mining to Support Science and Technology Management. Network Engineering and Information Society, World Engineers' Convention.
Zhu, D., & Porter, A. L. (2002). Automated extraction and visualization of information for technological intelligence and forecasting. Technological Forecasting & Social Change, 69(5), 495-506.