A Multi-criteria Group Decision Making Method for Selecting Big Data Visualization Tools


  • S. Grandhi CQUniversity, Australia.
  • S. Wibowo CQUniversity, Australia.


Big Data Visualization Tool, Evaluation and Selection, Decision Makers, Subjectiveness, Imprecision,


Big data visualization tools are providing opportunities for businesses to strengthen decision making and achieve competitive advantages. Evaluating and selecting the most suitable big data visualization tool is however challenging. To effectively deal with this issue, this paper presents a multicriteria group decision making method for evaluating and selecting of big data visualization tools. Intuitionistic fuzzy numbers are used to tackle the subjectiveness and imprecision of the decision making process. The concept based on ideal solutions is applied for producing a relative closeness coefficient value for every big data visualization tool alternative across all evaluation criteria. A big data visualization tool selection problem is presented to demonstrate the applicability of the method.


J. Valacich and C. Schneider, Information Systems Today: Managing in the Digital World, 6th edn. Australia: Pearson Education Limited, 2011.

C. Snijders, U. Matzat, and U. D. Reips, “Big data: Big gaps of knowledge in the field of Internet science,” Int. J. Internet Sci., vol. 7, no. 1, pp. 1-5, 2012.

S. Tsuchiya, Y. Sakamoto, Y. Tsuchimoto, and V. Lee, “Big data processing in cloud environments,” FUJITSU Sci. Technol., vol. 48, no. 2, pp. 159-168, 2012.

Peer Research, 2012, Big data analytics: intel’s it manager survey on how organisations are using big data, Intel, http://www.triforce.com.au/pdf/data-insights-peer-research-report.pdf.

C. L. P. Chen, and C. Zhang, “Data-intensive applications, challenges, techniques and technologies: A survey on big data,” Information Science, vol. 275, pp. 314-347, 2014.

M. Lnenicka, “AHP model for the big data analytics platform selection,” Acta Inform. Pragnesia, vol. 4, no. 2, pp. 108-121, 2015.

Dan Sommer, Rita L. Sallam, James Richardson, “Emerging technology analysis: Visualization-based data discovery tools,” June 17, 2011.

P. Zikopoulos, D. deRoos, C. Bienko, R. Buglio, and M. Andrews, Big Data Beyond the Hype: A Guide to Conversations for Today’s Data Center. USA: McGraw-Hill Education, 2015.

D. Loshin, Big Data Analytics: From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph. Waltham: Elsevier, 2013.

J. M. Tien, “Big data: Unleashing information,” J. Syst. Sci. Syst. Eng., vol. 22, no. 2, pp. 127-151, 2013.

G. M. Marakas and J.A. O'Brien, Introduction to Information Systems. New York: McGrawHill/Irwin, 2013.

J. S. Valacich, J. F. George, and J. A. Hoffer, Essentials of Systems Analysis and Design. New Jersey: Prentice Hall, 2012.

C. Lynch, “Big data: how do your data grow?,” Nature, vol. 455, pp. 28-29, 2008.

P. Lake and R. Drake, Information Systems Management in the Big Data Era. London: Springer, 2014.

Rinner, C. A Geographic Visualization Approach to Multi-Criteria Evaluation of Urban Quality of Life, Working Paper, VASDS (GIScience 2006)

S. Fuhrmann and W. Pike, User-centred Design of Collaborative Geovisualization Tools. In J. Dykes, A.M. MacEachren, and M.-J. Kraak, Exploring Geovisualization. Amsterdam: Elsevier, 2005.

E. L. Koua, A. M. MacEachren, and M. J. Kraak, “Evaluating the usability of visualization methods in an exploratory geovisualization environment,” Int. J. Geogr. Inform. Sci., vol. 20, no. 4, pp. 425-448, 2006.

S. S. Kara and N. Cheikhrouhou, “A multi criteria group decision making approach for collaborative software selection problem, J. Intell. Fuzzy Syst., vol. 26, pp. 37-47, 2014.

A. A. Zidan, B. B. Zaidan, M. Hussain, A. Haiqi, M. L. Mat Kiah, and M. Abdulnabi, “Multi-criteria analysis for OS-EMR software selection problem: A comparative study,” Decis. Support Syst., vol. 78, pp. 15- 27, 2015.

S. Wibowo and H. Deng, “Consensus-based decision support for multicriteria group decision making,” Comput. Ind. Eng., vol. 66, pp. 625-633, 2013.

K. Atanassov, “Intuitionistic fuzzy sets,” Fuzzy Sets Syst., vol. 20, pp. 87-96, 1986.

T. Y. Chen and C. H. Li, “Determining objective weights with intuitionistic fuzzy entropy measures: A comparative analysis,” Inform. Sci., vol. 180 no. 21, pp. 4207-4222, 2010.

Z. S. Xu, “Intuitionistic fuzzy aggregation operators,” IEEE Trans. Fuzzy Syst., vol. 15, pp. 1179-1187, 2007.

S. Wibowo and H. Deng, “Multi-criteria group decision making for evaluating the performance of e-waste recycling programs under

uncertainty,” Waste Manage., vol. 40, pp. 127-135, 2015.

R. Solanki, Q. Lohani, and P. K. Muhuri, “A novel clustering algorithm based on a new similarity measure over intuitionistic fuzzy sets,” in Proc. IEEE Int. Conf. Fuzzy Systems, IEEE, 2015, pp. 1-8.

N. N. Ramly, F. M. Nor, N. H. Ahmad, and M. H. Aziz, Comparative analysis on data visualization for operations dashboard,” Int. J. Inform. Educ. Technol., vol. 2, no. 4, Aug. 2012.




How to Cite

Grandhi, S., & Wibowo, S. (2018). A Multi-criteria Group Decision Making Method for Selecting Big Data Visualization Tools. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-8), 67–72. Retrieved from https://jtec.utem.edu.my/jtec/article/view/3737