企業倚賴資訊的程度與日俱增，資訊系統如因天災人禍導致系統服務中斷，勢必會對企業造成營運損失，因此，災害復原規劃(Disater Recovery Plan，DRP)已成為各企業必須面臨的重要課題。但如何規劃 DRP 牽涉到經驗及案例，要考慮的因素很多，比一般的系統需求如資料庫系統規劃複雜。DRP過去大部份依據專家的知識與經驗來規劃，容易造成規劃品質不一，經驗無法傳承，為解決此問題，本論文將比較災害復原規劃與系統生命週期理論間的差異，提出一個規則及案例知識來表達DRP，並發展一個結合規則式推理(RBR)及案例式推理(CBR)優點的 DRP推理法則雛形系統。我們以兩個實際專家案例來驗證我們的 DRP 知識表達與推理方法，結果顯示，本研究系統可依據使用者需求提供近似專家建議DRP的方案。

Businesses rely on the information more deeply day by day. If information system service interrupts due to disasters, it will make business operation loss. So, disaster recovery plan has become the critical issue that businesses must face. But how to plan DRP has been related to
experiences and case study about it. Because there are many issues that must be considered, DRP is
more complex than database plan. In previous stage, DRP was planned according to expert’s knowledge and experiences. It will be easy to make the plan’s quality inconsistent and experiences will not be transferred and retained. To resolve the problem, we will compare the difference between System Development Life Cycle and DRP. We will propose a rule and case knowledge representation for DRP, and then develop a hybrid rule and case’s DRP inference protype system with both advantages. At last, we use two practical experts’ cases to validate our DRP knowledge and inference method. The result shows the research system can provide the similar expert’s
solution according to the user’s requirement.

[1] 曾憲雄，專家系統導論工具應用，文魁資訊股份有限公司出版，2002。
[2] 精業股份有限公司，確保企業永續經營的異地備援，精業企業儲存技術專家特刊，2003。
[3] 許宏彬，異地備援及災害復原計劃 揭開異地備援九大架構，網路資訊雜誌，2002年5月，119-121。
[4] 謝清佳、吳琮璠 合著，資訊管理-理論與實務，智勝文化事業有限公司出版，1997。
[5] 張紹勳，人工智慧與專家系統，松崗電腦圖書資料股份有限公司出版，1993。
[6] 李家祺 編譯，人工智慧與專家系統，松崗電腦圖書資料股份有限公司出版，1988。
[7] 徐恩普，知識工程與專家系統，松崗電腦圖書資料股份有限公司出版，1990。
[8] 劉登榮編譯，MS-SQL Server 6.5技術手冊(I)，松格資訊有限公司出版，1997。
[9] Aamodt, A.,” A knowledge representation system for integration of general and case-specific knowledge,” Sixth International Conference on Tools with Artificial Intelligence, Nov 1994,
836–839.
[10] Aamodt A. and Plaza E., “Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches,” AICom - Artificial Intelligence Communications, IOS
Press, 7(1), 1994, 39-59
[11] Allemange, D., Review of EWCBR-93, AICom Press, 1993.
[12] Anderson, E. E., “Choice Models for the Evaluation and Selection of Software Packages”,
Journal of Management Information System, Spring 1990,123-138.
[13] Angeles, P. A., Dictionary of Philosophy, Barnes & Noble Books, 1981.
[14] Bryson K. M.; Millar H.; Joseph A. and Mobolurin A., “Using formal MS/OR modeling to support disaster recovery planning,” European Journal of Operational Research, 141, 2002,
679-688.
[15] Cercone, N.; Aijun An and Chan, C.,” Rule-induction and case-based reasoning: hybrid architectures appear advantageous,” IEEE Transactions on Knowledge and Data
Engineering,11(1), Jan/Feb 1999,166-174
[16] Eliason, A. L., System Development: Analysis, Design, and Implementation, Scott,
Foresman/Little, Brown Higher Education, 1989.
[17] Feigenbaum, E. A., “Themes and case studies of knowledge engineering,” Expert Systems in
the Micro-Electronic Age, Ed. D. Michie, Edinburg Univ. Press,1979,3-25.
[18] Friedl, W.J.,” The computer security framework,” IEEE 1990 International Carnahan
Conference on Security Technology, 1990, 92 –99.
[19] Garcia-Molina, H. and Polyzois, C.A.,” Issues in disaster recovery,” Compcon Spring ''90.
Intellectual Leverage. Digest of Papers. Thirty-Fifth IEEE Computer Society International
Conference. , 1990,573 –577.
[20] Giarratano, J. C. and Riley G. , Expert Systems： Principles and Programming, PWS
Publishing Company, 1998.
[21] Golding, A.R. and Rosenbloom, P. S., “Improving Accuracy by combining rule-based and
case-based reasoning,” Mitsubishi Electronic Research Laboratories, 1995, 1-40.
[22] Hatzilygeroudis, I. and Prentzas, J., “An Efficient Hybrid Rule Based Inference Engine with Explanation Capability,” the Proceedings of the 14th International FLAIRS Conference, Key
West, Florida, May 2001.
[23] Hatzilygeroudis, I. and Prentzas, J., “Integrating Hybrid Rule-Based with Case-Based
Reasoning,” S. Craw and A. Preece (Eds.):ECCBR, 2002, 336-349.
[24] Iyer, R.K. and Sarkis, J.,"Disaster recovery planning in an automated manufacturing
environment," IEEE Transactions on Engineering Management, 45(2), May 1998 ,163 –175.
[25] Jackson J., ”Give your lan a hand,” Security Management, 41(8), 1997, 44-52.
[26] Jackson P., Introduction to Expert Systems, Addison-Wesley Publishing Company, 1999.
[27] Jiang, M. F. and Tseng, S. S.,“Structural cases representation in Case-based Expert System,”
Proc. Of 4th KES International Conference, UK, 2000.
[28] Kolodner J. Case-Based Reasoning. Los Altos, CA: Morgan Kaufmann, 1993.
[29] Howard, L., “Time is of the essence,” Disaster Professional, 1997, 13-14.
[30] Lam, W., "Ensuring business continuity," IT Professiona l , 4(3) , May-June 2002, 19 –25.
[31] Lee K., Hwang S., Um K. and Park S.,” An extraction method of analogical information for efficient knowledge processing,” IEEE Region 10 Conference on Computer, Communication,
Control and Power Engineering, 2(0), Oct 1993, 644 –647.
[32] Minsky M., “A Framework for Representing Knowledge,” The Psychology of Computer Vision
, Patrick Winston, ed., McGraw-Hill, 1975, 211-217.
[33] Peter, W., ”Network Disaster Contingency Planning, “ Computers & Security，15(5), 1996,
411.
[34] Quillian, M. R., ”Semantic Memory,” Semantic Information Processing, Marvin Minsky, ed.,
MIT Press, 1968, 227-270.
[35] Rissland E. L., Daniels J. J., Rubinstein Z. B. and Skalak D. B., ”Case-based diagnostic analysis in a blackboard architecture,” In Proc. 11th National Conference on Artificial Intelligence, 1993, 66-72.
[36] Stanfill, C. and Waltz D., “The memory based reasoning paradigm,” Proceedings from a
workshop in Case Based reasoning, Clearwater Beach, Florida, Morgan Kaufmann Publ. ,May
1988,414-424.
[37] Waterman, D. A., A Guide to Expert Systems, Addison-Wesley Publishing Company, 1986.
[38] http://www.boma.gov.tw/law/86624186.htm, 2003/5/27
[39] http://www.sfc.gov.tw/reference/magazine/9109/Ss3.htm, 2003/5/27