Developing a Solution to the TRADOC Analysis Center’s Big Data Problem: A Big Data Opportunity
Author(s) -
Lee Bares,
Daniel P. Davis,
Daniel Min,
K. Raghava Rau,
Matthew Dabkowski
Publication year - 2019
Publication title -
industrial and systems engineering review
Language(s) - English
Resource type - Journals
ISSN - 2329-0188
DOI - 10.37266/iser.2018v6i2.pp82-87
Subject(s) - trac , big data , computer science , leverage (statistics) , globe , operations research , data science , data center , context (archaeology) , data collection , engineering , data mining , artificial intelligence , medicine , paleontology , biology , ophthalmology , programming language , operating system , statistics , mathematics
As data production, collection, and analytic techniques grow, emerging issues surrounding data management and storage challenge businesses and organizations around the globe. The US Army Training and Doctrine Command’s Analysis Center (TRAC) is no exception. For example, among TRAC's many tasks is the evaluation of new materiel solutions for the Army, which typically necessitates the use of computer simulation models such as COMBAT XXI. These models are computationally expensive, and they generate copious amounts of data, straining TRAC's current resources and forcing difficult, suboptimal decisions regarding data retention and analysis. This paper addresses this issue directly by developing "big data" solutions for TRAC and evaluating them using its organizational values. Framed in the context of a use case that prescribes system requirements, we leverage Monte Carlo simulation to account for inherent uncertainty and, ultimately, focus TRAC on several high potential alternatives.
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