Presentation + Paper
10 May 2019 Application of data science within the Army intelligence warfighting function: problem summary and key findings
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Abstract
Army Intelligence operates in a data rich environment with limited ability to operationalize exponentially increasing volumes of disparate structured and unstructured data to deliver timely, accurate, relevant, and tailored intelligence in support of mission command at echelon. The volume, velocity, variety, and veracity (the 4 Vs) of data challenge existing Army intelligence systems and processes, degrading the efficacy of the Intelligence Warfighting Function (IWfF). At the same time, industry has exploited the recent growth in data science technology to address the challenge of the 4 Vs and bring relevant data-driven insights to business leaders. To bring together the lessons from industry and the data science community, the US Army Research Laboratory (ARL) has collaborated with the US Army Intelligence Center of Excellence (USAICoE) to research these Military Intelligence (MI) challenges in an Army AR 5-5 Study entitled, “Application of Data Science within the Army Intelligence Warfighting Function.” This paper summarizes the problem statement, research performed, key findings, and way forward for MI to effectively employ data science and data scientists to reduce the burden on Army Intelligence Analysts and increase the effectiveness of data exploitation to maintain a competitive edge over our adversaries.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Robert Ganger, John Coles, Jacob Ekstrum, Timothy Hanratty, Eric Heilman, Jason Boslaugh, and Zachary Kendrick "Application of data science within the Army intelligence warfighting function: problem summary and key findings", Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110060N (10 May 2019); https://doi.org/10.1117/12.2519303
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Cited by 1 scholarly publication.
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KEYWORDS
Data modeling

Data processing

Statistical analysis

Analytical research

Visualization

Data communications

Mathematical modeling

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