A Fuzzy TOPSIS Approach for Big Data Analytics Platform Selection

• Main Authors: Salah Uddin, Mizanur Rahman, Samaun Hasan, S.M. Irfan Rana, Shaikh Muhammad Allayear
• Format: Article
• Language: English
• Published: Science and Research Branch,Islamic Azad University 2019-02-01
• Series: Journal of Advances in Computer Engineering and Technology
• Subjects: big data analytics; fuzzy topsis; multiple criteria decision making
• Online Access: http://jacet.srbiau.ac.ir/article_13637_e7c5f17733986ead559dd96d29f4fa26.pdf

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.