Title: Current trends in predictive analytics of big data
Authors: Tomasz Wiktor Wlodarczyk; Thomas J. Hacker
Department of Electrical and Computer Engineering, University of Stavanger, 4036 Stavanger, Norway
Computer and Information Technology, Purdue University, 47907 West Lafayette, USA
Abstract: Predictive analytics is a driving force motivating considerable interest in big data. Although there is clear interest in big data, the adoption rate of analytical techniques fuelled by big data that can extract knowledge and value from these data is less well understood. In this paper, we present a quantitative analysis of trends in publications related to predictive analytics, predictive modelling, big data and data intensive computing. Our evaluation shows an increasing popularity of big data in scientific publications, with ten-fold increase in the last three years. Concomitantly, we find that predictive analytics are connected with this trend, with two-fold increase in the last three years, but also a seven-fold increase in the same period when used in context with big data. We also classify the main application domains for big data and predictive analytics. Contrary to popular belief that big data is focused primarily on social media and business intelligence, our analysis found that almost half of scientific publications using predictive analytics were in healthcare, smart services, the internet of things, and weather and environment. Our results indicate the early adoption of big data-based analytics in these domains.
Keywords: big data; data intensive computing; predictive analytics; predictive modelling; elemental data; time series.
Int. J. of Big Data Intelligence, 2014 Vol.1, No.3, pp.172 - 180
Submission date: 16 Dec 2013
Date of acceptance: 10 Apr 2014
Available online: 16 Dec 2014