本研究確定了醫院基礎設施單元之間的相互依賴性。本研究的目的是找出醫院基礎設施的相互依賴性，並開發一種關鍵基礎設施相互依賴矩陣方法來預測和減少對醫院基礎設施系統的損壞。該研究對於醫院管理具有重要意義，因為與醫療服務相關的任何基礎設施的損壞對社會來說總是不方便，並且可能耗費大量社會資源且危及生命。
本研究從醫院獲得30天的數據，通過相互依賴矩陣方法進行分析和評估關鍵基礎設施。結果表明，所考慮的六個系統具有很強的相互關係和100％的相互依賴性。結果表明彼此之間的相互關聯程度，因此可以利用它來實現和保護系統免於因相互依賴而受到影響。本研究將有利未來利用大數據以改善醫院的關鍵基礎設施。

This study identifies hospital infrastructure units in terms of their dependencies on one another. The purpose of this research is to find out the hospital infrastructure interdependencies and develop a Critical Infrastructure Interdependency matrix method to predict and reduce damages to the hospital infrastructure system. 
       The study is significant to hospital administration because disruption of any infrastructure related to medical services is always inconvenient to the society and can be costly and life-threatening. A 30 days' data is obtained from hospitals which are analyzed and reviewed using python through a proposed Critical Infrastructure Interdependency matrix method. The results indicate that the six systems under consideration have a strong co-relationship and 100% interdependencies. The results denote the degree of co-relationship with one another and thus it can be utilized to implement and protect a system from being affected due to interdependency. This research will be beneficial for implementing Big Data for improved projects for the hospital projects in the future.

Acknowledgement	I
Table of Contents	IV
List of Tables	VI
List of Figures	VII
CHAPTER 1 INTRODUCTION	1
1.1	Background and Motivation	1
1.2	Research Objective	3
1.3	Research Approach	3
1.4	Structure of Dissertation	5
CHAPTER 2 LITERATURE REVIEW	7
2.1 Critical Infrastructure Independency (CII)	7
2.2 Inoperability Input-Output Model (IIM)	22
2.3 Big Data	28
2.3.1 Data Mining	28
2.3.2 General Sequence Pattern (GSP)	35
CHAPTER 3 METHODOLOGY	41
3.1 CII Matrix Method (CIIMM)	41
3.2 CII Matrix Method Algorithm (CIIMM)	41
CHAPTER 4 CASE PROJECT	43
4.1 Introduction of the Case Project	43
4.1.1 Data Pre-Processing	43
4.2 Data Implementation	45
4.2.1 Integrated the Data and Failure Information to a System	45
4.3 Data Analysis	47
4.3.1 Python	47
CHAPTER 5 CONCLUSIONS	49
5.1 Example and Discussion	49
5.2 Result	50
5.3 Conclusions	52
References	53


Table 2- 1 Summary of Critical Infrastructure Interdependency literature review	14
Table 2- 2 Summary of Inoperability Input-Output Method literature review	25
Table 2- 3 Summary of the Big Data and Data mining literature review	34
Table 2- 4 Summary of General Sequence Pattern Mining literature Review	40


Fig 1- 1 Research Flow Chart	3
Fig 2- 1 Dimensions for describing infrastructure interdependencies.	9
Fig 4- 1 OPC_Data File (data properties)	44
Fig 4- 2 Data details	45
Fig 5- 1 Raw data (.csv)	49
Fig 5- 2 Processed data	50
Fig 5- 3 Result output for day 2017-06-29	51
Fig 5- 4 Result output for day 2017-06-30	51
Fig 5- 5 Result output for day 2017-07-01	52

[1]	Agrawal, M. P., Pandey, V. C., & Keshri, S. P. (2013). An Analytical Study on Sequential Pattern Mining With Progressive Database, 1(3), 613–621.
[2]	Alavi, A. H., & Gandomi, A. H. (2017). Big data in civil engineering. Automation in Construction, 79, 1–2. https://doi.org/10.1016/j.autcon.2016.12.008
[3]	Arboleda, C. A., Abraham, D. M., & Lubitz, R. (2007). Simulation as a tool to assess the vulnerability of the operation of a health care facility. Journal of Performance of Constructed Facilities, 21(4), 302–312. https://doi.org/10.1061/(ASCE)0887-3828(2007)21:4(302)
[4]	Aremu, A. A., Aremu, A. O., & Adeaga, O. A. (2015). Assessment of critical equipment for preventative maintenance of plastic plant. American Society of Mechanical Engineers, Power Division (Publication) POWER, 2015–Janua, 1–7. https://doi.org/10.1115/POWER201549139
[5]	Ayres, J., Flannick, J., Gehrke, J., & Yiu, T. (2002). Sequential pattern mining using a bitmap representation. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Pp. 429-435). ACM.
[6]	Berkhin, P. (2003). Survey of Clustering Data Mining Techniques, 1–56.
[7]	Bhardwaj, B. K., & Pal, S. (2011). Data Mining : A prediction for performance improvement using classification. International Journal of Computer Science and Information Security, 9(4), 136–140.
[8]	Bilal, M., Oyedele, L. O., Qadir, J., Munir, K., Ajayi, S. O., Akinade, O. O., … Pasha, M. (2016). Big Data in the construction industry: A review of present status, opportunities, and future trends. Advanced Engineering Informatics, 30(3), 500–521. https://doi.org/10.1016/j.aei.2016.07.001
[9]	Chang, H. J., Hung, L. P., & Ho, C. L. (2007). An anticipation model of potential customers’ purchasing behavior based on clustering analysis and association rules analysis. Expert Systems with Applications, 32(3), 753-764.
[10]	Chou, C.-C., & Tseng, S.-M. (2010). Collection and Analysis of Critical Infrastructure Interdependency Relationships. ASCE, Journal of Computing in Civil Engineering, 24(6), 539–547. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000059
[11]	Chung-han, B. L. Y. and K. (2017). Power outage across Taiwan due to malfunction at power plant. Focus Taiwan News Channel.
[12]	Converse, A. O. (1971). On the extension of input-output analysis to account for environmental externalities. Am. Econ. Rev., 61, 197– 198.
[13]	Daniel Oliveira ; James H. Garrett, J. ; and L. S. (2009). Spatial Clustering Analysis of Water Main Break Events. Int. Workshop on Computing in Civil Engineering, ASCE, Reston, Va., 338–347.
[14]	Dueñas-osorio, L., Craig, J. I., Goodno, B. J., & Bostrom, A. (2007). Interdependent Response of Networked Systems, (September), 185–194.
[15]	Embrechts, P., Kluppelberg, C., and Mikosch, T. (1997). Modeling extremal events for insurance and finance. Ernst and Young Real Estate Advisory Group, New York.
[16]	Goel Research Scholar, A., Mallick, B., & Academics, D. (2015). Customer Purchasing Behavior using Sequential Pattern Mining Technique. International Journal of Computer Applications, 119(1), 975–8887.
[17]	Haimes, Y. Y., and Jiang, P. (2001). Leontief-based model of risk in complex interconnected infrastructures. J. Infrastruct. Syst., 7_1_, 1–12., 7(1), 1–12.
[18]	Haimes, Y. Y., Horowitz, B. M., Lambert, J. H., Santos, J., Lian, C., & Crowther, K. (2005). Inoperability Input-Output Model for Interdependent Infrastructure Sectors. I: Theory and Methodology. Journal of Infrastructure Systems, 11(2), 67–79. https://doi.org/10.1061/(ASCE)1076-0342(2005)11:2(80)
[19]	Isard, W. (1960). Methods of regional analysis: An introduction to regional science. MIT Press, Cambridge, Mass.
[20]	Kreng, V. B., & Yang, S.-W. (2013). Data mining of hospital characteristics in online publication of medical quality information. Health, 05(05), 931–937. https://doi.org/10.4236/health.2013.55123
[21]	Lahr, M. L., and Stevens, B. H. (2002). study of regionalization in the generation of aggregation error in regional input-output models. J. Regional Sci., 42~3!, 477–507.
[22]	Leontief, W. W. (1951a). Input-output economics. Sci. Am., October, 15–21.
[23]	Leontief, W. W. (1951b). The structure of the American economy. 1919– 1939, 2nd Ed., Oxford University Press, New York.
[24]	Leontief, W. W. (1966). Input-output economics. Oxford University Press, New York.
[25]	Leung, M., Haimes, Y. Y., & Santos, J. R. (2007). Supply- and Output-Side Extensions to the Inoperability Input-Output Model for Interdependent Infrastructures. Journal of Infrastructure Systems, 13(4), 299–310. https://doi.org/10.1061/(ASCE)1076-0342(2007)13:4(299)
[26]	Liew, C. J. (2000). The dynamic variable input-output model: An advancement from the Leontief dynamic input-output. Annals of Regional Science, 34~4!, 591–614.
[27]	Lindell, M. K., & Prater, C. S. (2004). Assessing Community Impacts of Natural Disasters, 4(4).
[28]	Linoff, G. S., & Berry, M. J. (2011). Data mining techniques: for marketing, sales, and customer relationship management. John Wiley & Sons.
[29]	Liu, M., & Xu, W. (2013). The approach for critical infrastructure sectors classification using the inoperability input-output model (IIM). Proceedings of 2013 6th International Conference on Information Management, Innovation Management and Industrial Engineering, ICIII 2013, 3(Iim), 7–10. https://doi.org/10.1109/ICIII.2013.6703668
[30]	Loggins, R. A., & Wallace, W. A. (2015). Rapid Assessment of Hurricane Damage and Disruption to Interdependent Civil Infrastructure Systems, 21(4), 1–12. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000249.
[31]	Mcdaniels, T., Chang, S., Peterson, K., Mikawoz, J., Reed, D., & Asce, M. (2007). Empirical Framework for Characterizing Infrastructure Failure Interdependencies, 13(3), 175–184.
[32]	Mendonça, D., & Wallace, W. A. (2006). Impacts of the 2001 World Trade Center Attack on New York City Critical Infrastructures, (December), 260–270.
[33]	Miao, R., & Shen, X. J. (2010). Construction of periodic temporal association rules in data mining. Proceedings - 2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010, 5(Fskd), 2133–2137. https://doi.org/10.1109/FSKD.2010.5569736
[34]	Miller, R. E., and Blair, P. D. (1985). Input-output analysis: Foundations and extensions, Prentice-Hall. Englewood Cliffs, N.J.
[35]	Miller, R. E., Polenske, K. R., and Rose, A. Z. (1989). Frontiers of input-output analysis. Oxford Univ. Press, New York.
[36]	Oh, E. H., Deshmukh, A., Hastak, M., & Asce, M. (2013). Criticality Assessment of Lifeline Infrastructure for Enhancing Disaster Response, (May), 98–107. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000084.
[37]	Ouyang, M., & Dueñas-Osorio, L. (2011). Efficient Approach to Compute Generalized Interdependent Effects between Infrastructure Systems. Journal of Computing in Civil Engineering, 25(5), 394–406. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000103
[38]	Padhy, N. (2012). Data Mining: A prediction Technique for the workers in the PR Department of Orissa (Block and Panchayat). International Journal of Computer Science, Engineering and Information Technology, 2(5), 197–36. https://doi.org/10.5121/ijcseit.2012.2503
[39]	Ren, J., & Zhou, X. (2006). A new incremental updating algorithm for mining sequential patterns. Journal of Computer Science, 2(4), 318–321. Retrieved from http://thescipub.com/abstract/10.3844/jcssp.2006.318.321
[40]	Rinaldi, S., Peerenboom, J., and Kelly, T. (2001). Identifying, understanding, and analyzing critical infrastructure independencies. IEEE Control Syst. Mag., 21, 11–25.
[41]	Rinaldi, S. M. (2004). Modeling and simulating critical infrastructures and their interdependencies. 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of The, 00(C), 1–8. https://doi.org/10.1109/HICSS.2004.1265180
[42]	Shih, C. Y., Scown, C. D., Soibelman, L., Matthews, H. S., Jr, J. H. G., Dodrill, K., & Mcsurdy, S. (2009). Data Management for Geospatial Vulnerability Assessment of Interdependencies in U . S . Power Generation, (September), 179–189.
[43]	Slimani, T., & Lazzez, A. (2014). Efficient Analysis of Pattern and Association Rule Mining Approaches. ArXiv Preprint ArXiv:1402.2892. Retrieved from http://arxiv.org/abs/1402.2892
[44]	Suguna, K., & Nandhini, K. (2015). Literature Review on Data Mining Techniques. Int.J.Computer Technology & Applications, 6(August), 583–585.
[45]	Tan, C. S., Tan, P. S., Lee, S. S. G., & Pham, M. T. (2014). An Inoperability Input-Output Model (IIM) for disruption propagation analysis. IEEE International Conference on Industrial Engineering and Engineering Management, (Iim), 186–190. https://doi.org/10.1109/IEEM.2013.6962400
[46]	Tiwari, P., & Shukla, N. (2012). Multidimensional Sequential Pattern Mining, 2(4), 2–4.
[47]	Wang, L., & Leite, F. (2013). Wang, L., & Leite, F. (2013). Knowledge discovery of spatial conflict resolution philosophies in BIM-enabled MEP design coordination using data mining techniques: a proof-of-concept. In Computing in Civil Engineering.
[48]	 Zhang, Y., Lindell, M., and Prater, C. (2009). “Vulnerability of community businesses to environmental disasters.” Disasters, 33(1), 38–57.