A Fuzzy TOPSIS Approach for Big Data Analytics Platform Selection
الموضوعات :
Salah Uddin
1
(Multimedia & Creative Technology, Daffodil International University)
Mizanur Rahman
2
(Department of Multimedia and Creative Technology, Daffodil International University, Dhaka, Bangladesh)
Samaun Hasan
3
(Department of Multimedia and Creative Technology, Daffodil International University, Dhaka, Bangladesh)
S.M. Irfan Rana
4
(Department of Multimedia and Creative Technology, Daffodil International University, Dhaka, Bangladesh)
Shaikh Muhammad Allayear
5
(Department of Multimedia and Creative Technology, Daffodil International University, Dhaka, Bangladesh)
الکلمات المفتاحية: Fuzzy TOPSIS, Multiple Criteria Decision Making, Big data analytics,
ملخص المقالة :
Big data sizes are constantly increasing. Big data analytics is where advanced analytic techniques are applied on big data sets. Analytics based on large data samples reveals and leverages business change. The popularity of big data analytics platforms, which are often available as open-source, has not remained unnoticed by big companies. Google uses MapReduce for PageRank and inverted indexes. Facebook uses Apache Hadoop to analyse their data and created Hive. eBay uses Apache Hadoop for search optimization and Twitter uses Apache Hadoop for log file analysis and other generated data[ 1]. Different Big data analytics platform providers are providing different types of facilities. To select those analytics platform for our business and public sector institutions purpose we follow multiple criteria. Multiple criteria decision making (MCDM) is mostly used in ranking one or more alternatives from finite set of available alternatives with respect to multiple criteria. Among many multi-criteria techniques, MAXMIN, MAXMAX, SAW, AHP, TOPSIS, SMART, ELECTRE are the most frequently used methods. The TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) methods are simplicity, rationality, comprehensibility, good computational efficiency and ability to measure the relative performance for each alternative in a simple mathematical form.
[1] Saecker, M., & Markl, V. (2. Big Data Analytics on Modern Hardware Architectures: A Technology Survey. In M. A. Aufaure & E. Zimányi (Eds.), Business Intelligence (pp. 125-149). Berlin Heidelberg: Springer..
[2] Elgendy, N., & Elragal, A. (2014). Big Data Analytics: A Literature Review Paper. In P. Perner (Ed.), ICDM 2014. LNAI, vol. 8557 (pp. 214-227). Heidelberg: Springer..
[3] Lněnička, M., & Komárková, J. . An Overview and Comparison of Big Data Analytics Platforms. In Sborník příspěvků z mezinárodní vědecké konference MMK 2014 (pp. 3446-3455). Hradec Králové: Magnanimitas.
[4] TIEN-CHIN WANG, HSIEN-DA LEE and CHUAN-CHENG WU. A Fuzzy TOPSIS Approach with Subjective Weights and Objective Weights.
[5] Mohammad Dabbagh, S.P.L. (2014) An Approach for Integrating the Prioritization of Functional and Nonfunctional Requirements. The Scientic World Journal, 2014, Article ID: 737626.
[6] Arfan Mansoor, Detlef Streitferdt, Franz-Felix Füßl. Alternatives Selection Using GORE Based on Fuzzy Numbers and TOPSIS.
[7] Chen-Tung, Ch., "Extensions of the TOPSIS for group decision-making under fuzzy environment",Fuzzy Sets and Systems, Volume 114, 2000.
[8] S. J. Chen and C. L. Hwang, Fuzzy Multiple Attribute Decision Making, (Lecture Notes in Economics and Mathematical System Series 375).Springer-Verlag. New York, 1992.
[9] C.-B. Cheng, 2004. Group Opinion Aggregation Based on a Grading Process: A Method for Constructing Triangular Fuzzy Numbers. Computers and Mathematics with Applications 48:1619-1632.
[10] D. Dubois and H. Prade. 1980. Fuzzy Sets and Systems: Theory and Applications. Academic Press, New York, 393 pp.
[11] C. Elkan, 1994. The Paradoxical Success of Fuzzy Logic. IEEE Expert: Intelligent Systems and Their Applications, 9(4):3-8.
[12] A. Kaufman, and M.M. Gupta. 1991. Introduction to Fuzzy Arithmetic, Theory and Application. Van Nostrand Reinhold Company, New York, 351 pp.
