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ABSTRACT
We used big data software Hadoop in Google News to collect complex high-velocity, high-volume terrorism information. We used big text search to code the factors of interest into nominal fields. We integrated new fields and records into an existing database drawn from other researchers. Our testable hypothesis was that there was a significant relationship between terrorist group ideology and terrorist attack type. Then we used correspondence analysis in SPSS to test our hypothesis. Our hypothesis was supported, so we developed a symmetric model to visualize the hidden relationships between terrorist ideology and attack type. Our purpose was to demonstrate how statistical software methods may be applied in big data analytics. These methods will generalize to other researchers and practitioners. The finding of a significant relationship between terrorist ideology and attack type may generalize to supply chain operations and national security planning.