A Data Farmer's Almanac
Sanchez, Susan M.
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An almanac conveys practical advice in the form of useful facts, advice, and forecasts. Data farming encapsulates the notion of purposeful data generation from simulation models. It uses large-scale designed experiments to facilitate growing simulation output in an efficient and effective fashion, and enables us to explore massive input spaces, uncover interesting features of complex response surfaces, and explicitly identify cause-and-effect relationships. In this presentation, I will weave the two halves of the title together as I recount some key concepts and developments in simulation experimentation, along with experiences and advice drawn from my own data-farming journey.
Proceedings of the 2016 Winter Simulation Conference, T. M. K. Roeder, P. I. Frazier, R. Szechtman, E. Zhou, T. Huschka, and S. E. Chick, eds.The article of record as published may be found at http://dx.doi.org/10.1109/WSC.2016.7822403
RightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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