Statistical Modeling and Visualizing Open Big Data Using a Terrorism Case Study

This study addressed the literature gap, identified by other researchers, that there are too few examples of applied empirical open big data analytics. Using correspondence analysis as a big data analytical technique, this study demonstrates how qualitative big data type could be analyzed to identify hidden factor relationships that may assist strategic decision making. We use a 64MB open meta big dataset developed by summarizing terrorist activity as keyword frequencies collected from trillions of public news articles published during a 43 year period from 1970-2013 and readily available statistical software, SPSS, to visually summarize the findings on a global terrorism big dataset. The approach in this paper might facilitate the research and development of open big data, big data analytics against global terrorism.