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dc.contributor.authorSanchez, Susan M.
dc.date.accessioned2015-08-21T22:38:54Z
dc.date.available2015-08-21T22:38:54Z
dc.date.issued2014
dc.identifier.citationSanchez, Susan M. "Simulation experiments: better data, not just big data." Proceedings of the 2014 Winter Simulation Conference. IEEE Press, 2014.en_US
dc.identifier.urihttp://hdl.handle.net/10945/46167
dc.description.abstractData mining tools have been around for several decades, but the term “big data” has only recently captured widespread attention. Numerous success stories have been promulgated as organizations have sifted through massive volumes of data to find interesting patterns that are, in turn, transformed into actionable information. Yet a key drawback to the big data paradigm is that it relies on observational data—limiting the types of insights that can be gained. The simulation world is different. A “data farming” metaphor captures the notion of purposeful data generation from simulation models. Large-scale designed experiments let us grow the simulation output efficiently and effectively. We can explore massive input spaces, uncover interesting features of complex simulation response surfaces, and explicitly identify cause-and-effect relationships. With this new mindset, we can achieve quantum leaps in the breadth, depth, and timeliness of the insights yielded by simulation models.en_US
dc.description.sponsorshipThis work was supported in part by the Naval Postgraduate School’s Acquisition Research Program and U.S. Marine Corps Expeditionary Energy Office.en_US
dc.format.extent12 p.
dc.publisherIEEE
dc.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.en_US
dc.titleSimulation Experiments: Better data, not just big dataen_US
dc.typeConference Paperen_US
dc.contributor.departmentOperations Research (OR)en_US


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