Summary of Data Farming

• Main Authors: Gary Horne, Klaus-Peter Schwierz
• Format: Article
• Language: English
• Published: MDPI AG 2016-03-01
• Series: Axioms
• Subjects: modeling and simulation; data generation; rapid scenario prototyping; distillation model development; design of experiments; high performance computing; data analysis and visualization; data mining; collaboration
• Online Access: http://www.mdpi.com/2075-1680/5/1/8

Data Farming is a process that has been developed to support decision-makers by answering questions that are not currently addressed. Data farming uses an inter-disciplinary approach that includes modeling and simulation, high performance computing, and statistical analysis to examine questions of interest with a large number of alternatives. Data farming allows for the examination of uncertain events with numerous possible outcomes and provides the capability of executing enough experiments so that both overall and unexpected results may be captured and examined for insights. Harnessing the power of data farming to apply it to our questions is essential to providing support not currently available to decision-makers. This support is critically needed in answering questions inherent in the scenarios we expect to confront in the future as the challenges our forces face become more complex and uncertain. This article was created on the basis of work conducted by Task Group MSG-088 “Data Farming in Support of NATO”, which is being applied in MSG-124 “Developing Actionable Data Farming Decision Support for NATO” of the Science and Technology Organization, North Atlantic Treaty Organization (STO NATO).