Data farming

Data farming is the process of using designed computational experiments to “grow” data, which can then be analyzed using statistical and visualization techniques to obtain insight into complex systems. These methods can be applied to any computational model.
Data farming differs from <a href="/facts/Data_mining/AH53q5ac">Data mining</a>, as the following metaphors indicate: 

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Miners seek valuable nuggets of ore buried in the earth, but have no control over what is out there or how hard it is to extract the nuggets from their surroundings. ... Similarly, data miners seek to uncover valuable nuggets of information buried within massive amounts of data. Data-mining techniques use statistical and graphical measures to try to identify interesting correlations or clusters in the data set.
Farmers cultivate the land to maximize their yield. They manipulate the environment to their advantage using irrigation, pest control, crop rotation, fertilizer, and more. Small-scale designed experiments let them determine whether these treatments are effective. Similarly, data farmers manipulate simulation models to their advantage, using large-scale designed experimentation to grow data from their models in a manner that easily lets them extract useful information. ...the results can reveal root cause-and-effect relationships between the model input factors and the model responses, in addition to rich graphical and statistical views of these relationships.

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A NATO modeling and simulation task group has documented the data farming process in the Final Report of MSG-088. 
Here, data farming uses collaborative processes in combining rapid scenario prototyping, simulation modeling, design of experiments, high performance computing, and analysis and visualization in an iterative loop-of-loops.

Data farming open-in-new

Data farming