Abstract
Today technology that learns from data to forecast future behavior of individuals, organizations, government and country as a whole, is playing a crucial role in the advancement of human race. In fact, the strategic advantage most of the companies today strive for are use of new available technologies like cloud computing and big data. However, today’s dynamic business environment poses severe challenges in front of companies as to how to make use of the power of big data with the technical flexibility that cloud computing provides? Therefore, evaluating, ranking and selecting the most appropriate cloud solution to manage big data project is a complex concern which required multi criteria decision environment. In this paper we propose a hybrid TOPSIS method combined with intuitionistic fuzzy set to select appropriate cloud solution to manage big data projects in group decision making environment. In order to collate individual opinions of decision makers for rating the importance of various criteria and alternatives, we employed intuitionistic fuzzy weighted averaging operator. Lastly sensitivity analysis is performed so as to evaluate the impact of criteria weights on final ranking of alternatives.
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References
Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20:87–96
Atanassov KT (1999) Intuitionistic fuzzy sets. Springer, Heidelberg
Atanassov K, Pasi G, Yager RR (2005) Intuitionistic fuzzy interpretations of multi-criteria multi-person and multi-measurement tool decision making. Int J Syst Sci 36(14):859–868
Chen SM, Tan JM (1994) Handling multi criteria fuzzy decision-making problems based on vague set theory. Fuzzy Sets Syst 67:163–172
Chen CLP, Zhang CY (2014) Data intensive applications, challenges, techniques, and technologies: a survey on Big Data. Inf Sci 275:314–347
De SK, Biswas R, Roy AR (2001) An application of intuitionistic fuzzy sets in medical diagnosis. Fuzzy Sets Syst 117:209–213
Demirkan H, Delen D (2013) Leveraging the capabilities of service-oriented decision support systems: putting analytics and big data in cloud. Decis Support Syst 55:412–421
Hashem IAT, Yaqoob I, Anuar NB, Mokhtar S, Gani A, Khan SU (2015) The rise of big data on cloud computing: review and open research issues. Inf Syst 47:98–115
Liu HW, Wang GJ (2007) Multi-criteria decision-making methods based on intuitionistic fuzzy sets. Eur J Oper Res 179:220–233
Lynch C (2008) Big data: how do your data grow? Nature 455:28–29
Ruiz-Alvarez A, Humphrey M (2011) An automated approach to cloud storage service selection. In: Proceedings of the 2nd international workshop on scientific cloud computing—ScienceCloud’11. doi:10.1145/1996109.1996117
Sachdeva N, Singh O, Kapur PK (2015) Modeling critical success factors for adoption of big data analytics project: an ISM-MICMAC based analysis. Commun Dependabil Qual Manag Int J 18(4):93–110
Shu MS, Cheng CH, Chang JR (2006) Using intuitionistic fuzzy sets for fault tree analysis on printed circuit board assembly. Microelectron Reliab 46(12):2139–2148
Snijders C, Matzat U, Reips UD (2012) Big data: big gaps of knowledge in the field of Internet science. Int J Internet Sci 7(1):1–5
Szalay A, Gray J (2006) Science in an exponential world. Nature 440:413–414
Szmidt E, Kacprzyk J (2000) Distances between intuitionistic fuzzy sets. Fuzzy Sets Syst 114:505–518
Szmidt E, Kacprzyk J (2001) Intuitionistic fuzzy sets in some medical applications. Lect Notes Comput Sci 2206:148–151
Szmidt E, Kacprzyk J (2004) A similarity measure for intuitionistic fuzzy sets and its application in supporting medical diagnostic reasoning. Lect Notes Comput Sci 3070:388–393
Talia D (2013) Clouds for scalable big data analytics. Computer 46:98–101
Tsuchiya S, Sakamoto Y, Tsuchimoto Y, Lee V (2012) Big data processing in cloud environments. FUJITSU Sci Technol J 48(2):159–168
Vishwakarma Y, Sharma SP (2016) Uncertainty analysis of an industrial system using Intuitionistic Fuzzy Set Theory. Int J Syst Assur Eng Manag 7(1):73–83
Vlachos IK, Sergiadis GD (2007) Intuitionistic fuzzy information—applications to pattern recognition. Pattern Recogn Lett 28:197–206
Wang P (2009) QoS-aware web services selection with intuitionistic fuzzy set under consumer’s vague perception. Expert Syst Appl 36(3):4460–4466
Wang WQ, Xin XL (2005) Distance measure between intuitionistic fuzzy sets. Pattern Recogn Lett 26:2063–2069
Wang X, Cao J, Xiang Y (2015) Dynamic cloud service selection using an adaptive learning mechanism in multi-cloud computing. J Syst Softw 100:195–210
Whaiduzzaman M, Gani A, Anuar NB, Shiraz M, Haque MN, Haque IT (2014) Cloud service selection using multi-criteria decision analysis. Sci World J. doi:10.1155/2014/459375
Xu ZS (2007) Intuitionistic fuzzy aggregation operators. IEEE Trans Fuzzy Syst 15(6):1179–1187
Xu ZS, Yager RR (2006) Some geometric aggregation operators based on intuitionistic fuzzy sets. Int J Gen Syst 35:417–433
Xu ZS, Yager RR (2008) Dynamic intuitionistic fuzzy multi-attribute decision making. Int J Approx Reason 48:246–262
Zhang CY, Fu HY (2006) Similarity measures on three kinds of fuzzy sets. Pattern Recogn Lett 27:1307–1317
Zhang L, Wu C, Li Z, Guo C, Chen M, Lau FCM (2013) Moving big data to the cloud: an online cost-minimizing approach. Sel Areas Commun IEEE J 31:2710–2721
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Sachdeva, N., Singh, O., Kapur, P.K. et al. Multi-criteria intuitionistic fuzzy group decision analysis with TOPSIS method for selecting appropriate cloud solution to manage big data projects. Int J Syst Assur Eng Manag 7, 316–324 (2016). https://doi.org/10.1007/s13198-016-0455-x
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DOI: https://doi.org/10.1007/s13198-016-0455-x