A Data Farmer's Almanac
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Authors
Sanchez, Susan M.
Subjects
Advisors
Date of Issue
2016
Date
2016
Publisher
IEEE
Language
Abstract
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.
Type
Conference Paper
Description
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
The article of record as published may be found at http://dx.doi.org/10.1109/WSC.2016.7822403
Series/Report No
Department
Operations Research (OR)
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
2 p.
Citation
Sanchez, Susan M. "A data farmer's almanac." In Winter Simulation Conference (WSC), 2016, pp. 3-3. IEEE, 2016.
Distribution Statement
Rights
This 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.