博碩士論文 109453049 詳細資訊

以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:17 、訪客IP:
姓名 余雅婷(Ya-Ting Yu)  查詢紙本館藏   畢業系所 資訊管理學系在職專班
論文名稱 應用案例式推理於問題管理系統之研究 -以筆記型電腦產品為例
(Applying Case-Based Reasoning Approach to Issue Management System - A Case Study of Laptop Computer Product)
★ 專案管理的溝通關鍵路徑探討─以某企業軟體專案為例★ 運用並探討會議流如何促進敏捷發展過程中團隊溝通與文件化:以T銀行系統開發為例
★ 專案化資訊服務中人力連續派遣決策模式之研究─以高鐵行控資訊設備維護為例★ 以組織正義觀點介入案件指派決策之研究
★ 應用協調理論建立系統軟體測試中問題改善之協作流程★ 運用限制理論於多專案開發模式的人力資源配置之探討
★ 應用會議流方法於軟體專案開發之個案研究:以翰昇科技公司為例★ 多重專案、多期再規劃的軟體開發接案決策模式:以南亞科技資訊部門為例
★ 會議導向敏捷軟體開發及系統設計:以大學畢業專題為例★ 一種基於物件、屬性導向之變更影響分析方法於差異化產品設計
★ 會議流方法對大學畢業專題的團隊合作品質影響之實驗研究★ 實施敏捷式發展法於大學部畢業專題之 行動研究 – 以中央大學資管系為例
★ 建立一個用來評核自然語言需求品質的線上資訊系統★ 結合本體論與模糊分析網路程序法於軟體測試之風險與風險關聯辨識
★ 在軟體反向工程中針對UML結構模型圖之線上品質評核系統★ 以模糊專家系統實作軟體專案調適準則
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 ( 永不開放)
摘要(中) 隨著全球環境的變遷,加上新型冠狀肺炎(COVID-19)疫情影響,改變了全球人類工作模式,進而影響筆記型電腦市場需求不斷增加,使工廠有生產製造
的時間壓力。 為確保產品品質管理從驗證到售後服務的問題都能準確地被檢索出正確的知識資訊,企業將透過問題管理系統 (IMS)追求效率與快速解決問
題 ,以輔助組織管理品質以及企業使用者管控品質配合效率。因為案例式推理能檢索到相似問題以及知識的再利用,而達到知識快速並精準分享,因此,本研究嘗試運用案例式推理 (Case-based reasoning , CBR) 來改善現有問題管理系統之檢索與知識重用,將知識應用於類似的新問題,以及設計智能化服務於問題管理系統,使企業面臨的人工客服成本問題,達到降低客服的工作量,使FAE客服工程師提升筆記型電腦產品問題的解決效率,讓時間價值最大化。
摘要(英) As the global environment changes and the impact of the new coronary pneumonia (COVID-19) epidemic changes global human work patterns, it affects the increasing demand for notebook computers and puts time pressure on factories to produce. To ensure that product quality management issues, from validation to after-sales service, are accurately retrieved with the correct knowledge, companies will pursue efficiency and rapid problem resolution through an Issue Management System (IMS) to assist organizations in managing quality and business users in controlling quality with efficiency. Because case-based reasoning can retrieve similar problems and reuse knowledge to achieve rapid and accurate knowledge sharing, this study tries to apply case-based reasoning (CBR) to improve the retrieval and knowledge reuse of existing problem management systems, apply knowledge to similar new problems, and design intelligent services for problem management systems, Therefore, enterprises can design intellect services for a problem management system which is allowed them to reduce the cost of the customer service as well as the workload. Furthermore, enable FAE customer service engineers to improve the efficiency of troubleshooting by using notebook products, leading to a decrease in the work.
關鍵字(中) ★ 案例式推理(CBR)
★ 問題管理系統
★ 軟體系統
★ 智能化服務
關鍵字(英) ★ Case-Based Reasoning (CBR)
★ Problem Management System
★ Software System
★ Intelligent Services
論文目次 摘要.........vii
Abstract .........viii
1.1 研究背景 ............1
1.2 研究動機與問題 .......2
1.3 研究目的 ............2
1.4 論文架構 ........... 3
2.1 問題管理系統 ......... 5
2.2 案例式推理概念與相關應用 ... 7
2.3 系統檢索相關應用 ......... 10
2.4 小結 ......... 13
3.1 系統架構 ......... 15
3.2 系統設計 ......... 16
3.3 建置檢索問題解決案例庫 ...... 23
3.4 案例相似計算方法 ......... 25
4.1 個案描述 .......... 29
4.2 系統開發工具 ......... 30
4.3 系統展示 .......... 31
5.1 系統驗證 .........35
5.2 訪談與分析 ......... 38
5.3 研究限制與未來發展 ....... 42
參考文獻 1. 國際數據資訊 (2021) , IDC(國際數據資訊)研究顯示:疫情延燒持續挹注PC裝置需求,上游供應鏈缺料為影響出貨關鍵,取自: https://www.idc.com/getdoc.jsp?containerId=prAP47903921 (Retrieved on: 2021/06/07) 2. 朱福喜 , 朱三元 , & 伍春香 . ( 人工智能基础教程 . 清华大学出版
3. 陈二静 , & 姜恩波 . (2017). 文本相似度计算方法研究综述 数据分析与
知识发现 1(6), 1-11.
4. 張云濤 , & 龔玲 ( 資料探勘原理與技術 . 五南圖書出版股份有限
5. 個案公司企業社會責任報告,取自:
6. Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1), 39-59.
7. Ahn, J., Park, M., Lee, H. S., Ahn, S. J., Ji, S. H., Song, K., & Son, B. S. (2017). Covariance effect analysis of similarity measurement methods for early construction cost estimation using case-based reasoning. Automation in Construction, 81(1), 254-266.
8. Ambos, T. C., & Ambos, B. (2009). The impact of distance on knowledge transfer effectiveness in multinational corporations. Journal of International Management, 15(1), 1-14.
9. Anthony Jnr, B. (2021). A case-based reasoning recommender system for sustainable smart city development. AI & SOCIETY, 36, 159-183.
10. Alarifi, A., Tolba, A., Al-Makhadmeh, Z., & Said, W. (2020). A big data approach to sentiment analysis using greedy feature selection with cat swarm optimization-based long short-term memory neural networks. The Journal of Supercomputing, 76(6), 4414-4429.
11. Aronczyk, M. (2018). Public relations, issue management, and the transformation of American environmentalism, 1948–1992. Enterprise & Society, 19(4), 836-863.
12. Baeza-Yates, R., & Ribeiro-Neto, B. (1999). Modern Information Retrieval (Vol. 463). New York: ACM press.
13. Balamurugan, S. (2016). Feature Selection for Supervised Learning via Dependency Analysis. Journal of Computational and Theoretical Nanoscience, 13(10), 6885-6891.
14. Babcock, L., & Vallesi, A. (2015). The interaction of process and domain in prefrontal cortex during inductive reasoning. Neuropsychologia, 67, 91-99.
15. Behbahani, M., Saghaee, A., & Noorossana, R. (2012). A case-based reasoning system development for statistical process control: Case representation and retrieval. Computers & Industrial Engineering, 63(4), 1107-1117.
16. Briand, L. (2012). Embracing the engineering side of software engineering. IEEE software, 29(4), 96-96.
17. B. Curtis, H. Krasner, N. Iscoe, A field study of the software design process for large systems, Communications of the ACM, 31 (1988) 1268-1287.
18. Bench-Capon, T. J. (2017). HYPO’s legacy: introduction to the virtual special issue. Artificial Intelligence and Law, 25(2), 205-250.
19. Camarillo, A., Ríos, J., & Althoff, K. D. (2017). CBR and PLM applied to diagnosis and technical support during problem solving in the Continuous Improvement Process of manufacturing plants. Procedia Manufacturing, 13, 987-994.
20. Carnevale, J. B., & Hatak, I. (2020). Employee adjustment and well-being in the era of COVID-19: Implications for human resource management. Journal of Business Research, 116, 183-187.
21. Cha, S. H. (2007). Comprehensive survey on distance/similarity measures between probability density functions. City, 1(2), 1.
22. Ciervo, J., Shen, S. C., Stallcup, K., Thomas, A., Farnum, M. A., Lobanov, V. S., & Agrafiotis, D. K. (2019). A new risk and issue management system to improve productivity, quality, and compliance in clinical trials. JAMIA Open, 2(2), 216-221.
23. Cummings, J. N. (2004). Work groups, structural diversity, and knowledge sharing in a global organization. Management science, 50(3), 352-364.
24. Cross, V. (1994). Fuzzy information retrieval. Journal of Intelligent Information Systems, 3(1), 29-56.
25. De Vasconcelos, J. B., Kimble, C., Carreteiro, P., & Rocha, Á. (2017). The application of knowledge management to software evolution. International Journal of Information Management, 37(1), 1499-1506.
26. De Mantaras, R. L., McSherry, D., Bridge, D., Leake, D., Smyth, B., Craw, S., ... & Watson, I. (2005). Retrieval, reuse, revision and retention in case-based reasoning. The Knowledge Engineering Review, 20(3), 215-240.
27. Demigha, S., & Rolland, C. (2003, May). Training-aided system in senology: methodologies and techniques. In Medical Imaging 2003: PACS and Integrated Medical Information Systems: Design and Evaluation (Vol. 5033, pp. 339-349).
28. Demigha, S. (2018, September). Big Data, Knowledge Management (KM) and Case-Based Reasoning (CBR). In European Conference on Knowledge Management (pp. 164-XVII). Academic Conferences International Limited. (United States-US)
29. Deza, M. M., & Deza, E. (2009). Encyclopedia of distances. In Encyclopedia of distances (pp. 1-583). Springer, Berlin, Heidelberg.
30. Deming, W. E. (1986). Out of crisis, centre for advanced engineering study. Massachusetts Institute of Technology, Cambridge, MA, 367-388.
31. Ghobadi, S. (2015). What drives knowledge sharing in software development teams: A literature review and classification framework. Information & Management, 52(1), 82-97.
32. González-Briones, A., Rivas, A., Chamoso, P., Casado-Vara, R., & Corchado, J. M. (2018, June). Case-based reasoning and agent based job offer recommender system. In The 13th International Conference on Soft Computing Models in Industrial and Environmental Applications (pp. 21-33). Springer, Cham.( san sebastian, Spain)
33. Grobelnik, M., Milič-Frayling, N., & Mladenić, D. (Eds.). (2002). Proceedings of the ICML-2002 Workshop on Text Learning. University of New South Wales.
34. Guo, Y., Zhang, B., Sun, Y., Jiang, K., & Wu, K. (2021). Machine learning based feature selection and knowledge reasoning for CBR system under big data. Pattern Recognition, 112, 107805.
35. Henderson, C. (2006). Building Scalable Web Sites: Building, scaling, and optimizing the next generation of web applications. " O′Reilly Media, Inc.".
36. Imama, C., & Indriyanti, A. D. (2013). Penerapan Case Based Reasoning Dengan Algoritma Nearest Neighbor Untuk Analisis Pemberian Kredit Di Lembaga Pembiayaan. Jurnal Manajemen Informatika, 2(01), 11-21.
37. Ishikawa, K., & ISHIKAWA, K. A. (1985). What is total quality control? The Japanese way. Prentice Hall.
38. Jena, P. R., Majhi, R., & Majhi, B. (2015). Development and performance evaluation of a novel knowledge guided artificial neural network (KGANN) model for exchange rate prediction. Journal of King Saud University-Computer and Information Sciences, 27(4), 450-457.
39. Ji, S. H., Park, M., & Lee, H. S. (2012). Case adaptation method of case-based reasoning for construction cost estimation in Korea. Journal of Construction Engineering and Management, 138(1), 43-52.
40. Juran, J. M., & Gryna, F. M. (1974). Quality control handbook (No. 658.562 Q-1q). McGraw Hill,.
41. J. L. Kolodner, "Case-Based Reasoning," San Mateo, CA: Morgan Kaufmann Publishers, Inc., 1993.
42. Kim, J.H. and Kim, M. (2020), “Conceptualization and assessment of E-service quality for luxury brands”, The Service Industries Journal, Vol. 40 Nos 5-6, pp. 436-470.
43. Kolodner, J. (2014). Case-based reasoning. Morgan Kaufmann.
44. Ke, C., Jiang, Z., Zhang, H., Wang, Y., & Zhu, S. (2020). An intelligent design for remanufacturing method based on vector space model and case-based reasoning. Journal of Cleaner Production, 277, 123269. 45. Kraft, D. H., & Colvin, E. (2017). Fuzzy information retrieval. Synthesis Lectures on Information Concepts, Retrieval, and Services, 9(1), i-63.
46. Kwon, N., Lee, J., Park, M., Yoon, I., & Ahn, Y. (2019). Performance evaluation of distance measurement methods for construction noise prediction using case-based reasoning. Sustainability, 11(3), 871.
47. Lahitani, A. R., Permanasari, A. E., & Setiawan, N. A. (2016, April). Cosine similarity to determine similarity measure: Study case in online essay assessment. In 2016 4th International Conference on Cyber and IT Service Management (pp. 1-6). IEEE.( Bandung, Indonesia)
48. Larsen, B., & Aone, C. (1999, August). Fast and effective text mining using linear-time document clustering. In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 16-22).( California, San Diego, USA)
49. Lao, S. I., Choy, K. L., Ho, G. T., Yam, R. C., Tsim, M. Y., & Poon, T. C. (2012). Achieving quality assurance functionality in the food industry using a hybrid case-based reasoning and fuzzy logic approach. Expert Systems with Applications, 39(5), 5251-5261.
50. Lamy, J. B., Sekar, B., Guezennec, G., Bouaud, J., & Séroussi, B. (2019). Explainable artificial intelligence for breast cancer: A visual case-based reasoning approach. Artificial intelligence in medicine, 94, 42-53.
51. Liu, J., Li, H., Skitmore, M., & Zhang, Y. (2019). Experience mining based on case-based reasoning for dispute settlement of international construction projects. Automation in Construction, 97(1), 181-191.
52. Lenfle, S. (2016). Floating in space? On the strangeness of exploratory projects. Project Management Journal, 47(2), 47-61. 53. Lin, K. S. (2020). A case-based reasoning system for interior design using a new cosine similarity retrieval algorithm. Journal of Information and Telecommunication, 4(1), 91-104.
54. Liao, Z., Zhou, C., Tian, W., Hu, T., & Guo, R. (2019). CBR-based integration of a hydrodynamic and water quality model and GIS—a case study of Chaohu City. Environmental Science and Pollution Research, 26(7), 6436-6449.
55. Méndez, N. D. D., Marín, P. A. R., & Carranza, D. A. O. (2018). Intelligent Personal Assistant for Educational Material Recommendation Based on CBR. In Personal Assistants: Emerging Computational Technologies (pp. 113-131). Springer, Cham. 56. Microsoft (2018). State of Global Customer Service Report.
57. Mossalam, A. (2018). Projects’ issue management. HBRC journal, 14(3), 400-407. 58. Nahm, U. Y., Bilenko, M., & Mooney, R. J. (2002, July). Two approaches to handling noisy variation in text mining. In Proceedings of the ICML-2002 workshop on text learning (TextML’2002) (pp. 18-27). 59. Neysiani, B. S., & Babamir, S. M. (2019, April). New methodology for contextual features usage in duplicate bug reports detection: dimension expansion based on manhattan distance similarity of topics. In 2019 5th international conference on web research (ICWR) (pp. 178-183). IEEE. (Tehran, Iran) 60. Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of management information systems, 24(3), 45-77.
61. Price, C. J., & Pegler, I. S. (1995, January). Deciding parameter values with case-based reasoning. In UK Workshop on Case-Based Reasoning (pp. 119-133). Springer, Berlin, Heidelberg.
62. Project Mangement Institute. (2009). Practice standard for project risk management. Project Management Institute.
63. Project Management Institute (PMI). (2013). A guide to the project management body of knowledge (PMI® Guide 5th Edition). Project Management Institute, Inc.
64. Pinto, T., Faia, R., Navarro-Caceres, M., Santos, G., Corchado, J. M., & Vale, Z. (2018). Multi-agent-based CBR recommender system for intelligent energy management in buildings. IEEE Systems Journal, 13(1), 1084-1095.
65. Peña, F. J., Roldán, M. L., & Vegetti, M. M. Identification of user stories in software issues records applying pre-trained natural language processing models. 66. Polkowski, L., Skowron, A., & Komorowski, J. (1996, April). Approximate case-based reasoning: A rough mereological approach. In Proc. of the 4-th German Workshop on Case-Based Reasoning, System Developments and Evaluation (pp. 144-151). 67. Rani, P., & Vashishtha, J. (2017). An appraise of KNN to the perfection. Int J Comput Appl, 170(2), 13-7. 68. Rahman, A., & Qosim, A. (2021). Sistem Cerdas Pengelompokan Mahasiswa Berdasarkan Prediksi Performa Belajar Dengan Metode Case Based Reasoning. Jurnal Edik Informatika Penelitian Bidang Komputer Sains dan Pendidikan Informatika, 8(1), 13-26. 69. Rahutomo, F., Kitasuka, T., & Aritsugi, M. (2012, October). Semantic cosine similarity. In The 7th International Student Conference on Advanced Science and Technology ICAST ,4(1), p. 1.(Seoul, South Korea)
70. R. L. d. Mántaras et al., “Retrieval, reuse, revision and retention in CBR,” Knowledge Eng. Review, 20(3), pp. 215-240, 2005. 71. Richter, M. M., & Weber, R. O. (2013). Basic CBR elements. In Case-Based Reasoning (pp. 17-40). Springer, Berlin, Heidelberg.
72. S. Faraj, L. Sproull, Coordinating expertise in software development teams,Management Science, 46 (2000) 1554-1568.
73. S. Sawyer, P.J. Guinan, J. Cooprider, Social interactions of information systems development teams: a performance perspective, Information Systems Journal, 20 (2010) 81-107 74. Sallis, E. (2014). Total quality management in education. Routledge. 75. Santoro, G., Vrontis, D., Thrassou, A., & Dezi, L. (2018). The Internet of Things: Building a knowledge management system for open innovation and knowledge management capacity. Technological forecasting and social change, 136(2018), 347-354. 76. Schmidt, G. (1998). Case-based reasoning for production scheduling. International journal of production economics, 56, 537-546.
77. Shank, R., & Abelson, R. (1977). Scripts, plans, goals and understanding.
78. Schank, R. C., & Abelson, R. P. (2013). Scripts, plans, goals, and understanding: An inquiry into human knowledge structures. Psychology Press.
79. Siraj, N. B., & Fayek, A. R. (2019). Risk identification and common risks in construction: Literature review and content analysis. Journal of Construction Engineering and Management, 145(9), 03119004. 80. Shin, K. S., & Han, I. (2001). A case-based approach using inductive indexing for corporate bond rating. Decision Support Systems, 32(1), 41-52. 81. Shalwani, A., & Lines, B. C. (2020). An empirical analysis of issue management in small building construction projects. International Journal of Construction Education and Research, 1-21. 82. Schott, P., Lederer, M., Eigner, I., & Bodendorf, F. (2020). Case-based reasoning for complexity management in Industry 4.0. Journal of Manufacturing Technology Management. 83. Sternberg, R. J., Sternberg, K., & Mio, J. (2012). Cognitive psychology.Cengage Learning Press.
84. Tang, V., Choy, K. L., Ho, G. T., Lam, H. Y., & Tsang, Y. P. (2019). An IoMT-based geriatric care management system for achieving smart health in nursing homes. Industrial Management & Data Systems. 85. Thompson, V. U., Panchev, C., & Oakes, M. (2015, November). Performance evaluation of similarity measures on similar and dissimilar text retrieval. In 2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) (Vol. 1, pp. 577-584). IEEE.( Lisbon, Portugal)
86. Watson, I., & Marir, F. (1994). Case-based reasoning: A review. The knowledge engineering review, 9(4), 327-354. 87. Veitía, F. J. P., Roldán, L., & Vegetti, M. (2020, December). User Stories identification in software′s issues records using natural language processing. In 2020 IEEE Congreso Bienal de Argentina (ARGENCON) (pp. 1-7). IEEE. 88. Xia, P., Zhang, L., & Li, F. (2015). Learning similarity with cosine similarity ensemble. Information Sciences, 307, 39-52. 89. Xiang, S., Nie, F., & Zhang, C. (2008). Learning a Mahalanobis distance metric for data clustering and classification. Pattern recognition, 41(12), 3600-3612. 90. Yao, L., Mao, C., & Luo, Y. (2019). Clinical text classification with rule-based features and knowledge-guided convolutional neural networks. BMC medical informatics and decision making, 19(3), 31-39. 91. Yang, J., & Delpha, C. (2022). An incipient fault diagnosis methodology using local Mahalanobis distance: Detection process based on empirical probability density estimation. Signal Processing, 190, 108308. 92. Zhai, Z., Martínez, J. F., Martínez, N. L., & Díaz, V. H. (2020). Applying case-based reasoning and a learning-based adaptation strategy to irrigation scheduling in grape farming. Computers and Electronics in Agriculture, 178, 105741.
93. Zadrożny, S., & Nowacka, K. (2009). Fuzzy information retrieval model revisited. Fuzzy Sets and Systems, 160(15), 2173-2191. 94. Zhai, Z., Ortega, J. F. M., Castillejo, P., & Beltran, V. (2019). A triangular similarity measure for case retrieval in CBR and its application to an agricultural decision support system. Sensors, 19(21), 4605. 95. Zhang, L. (2021). Research on case reasoning method based on TF-IDF. International Journal of System Assurance Engineering and Management, 12(3), 608-615. 96. Zhang, L., & Qi, P. (2021). Research on Key Technologies of Personalized Intervention for Chronic Diseases Based on Case-Based Reasoning. Computational and Mathematical Methods in Medicine, 2021.
指導教授 陳仲儼(Chung-Yang Chen) 審核日期 2022-6-29
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明