BIG DATA ANALYTICS: A TEXT MINING-BASED LITERATURE ANALYSIS

Authors

  • Ahmed Elragal Department of Computer Science, Electrical and Space Engineering Luleå Tekniska Universitet, Luleå, Sweden
  • Moutaz Haddara Department of Computer Science, Electrical and Space Engineering Luleå Tekniska Universitet, Luleå, Sweden Westerdals- Oslo School of Arts, Communication and Technology, Oslo, Norway

Abstract

This literature review paper summarizes the state-of-the-art research on big data analytics. Due to massive amount of data exchanged everyday and the increased need for better data-based decision, businesses nowadays are looking for ways to efficiently manage, and optimize these huge datasets. Moreover, because of globalization, partnerships, value networks, emergence of social networks, and the huge information flow across and within enterprises, more and more businesses are interested in utilizing big data analytics. The main focus of this paper is to elucidate knowledge on the characteristics of big data analytics literature as well as explore the areas that lack sufficient research within the big data analytics domain, suggest future research avenues, as well as, present the current research findings that could aid practitioners, researchers, and vendors when embarking on big data analytics projects. Towards that end, we have reviewed 24 publications between 2010 and 2014. Results of text mining the papers revealed that they belong to three clusters with both common as well as distinct characteristics. The reviewed papers were clustered into three main themes, 1) technical algorithsms; 2) processing, cloud computing, opportunities & challenges; and 3) performance, prediction, and distributed systems. 

Issue

Section

NOKOBIT artikler