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While big data relies on sifting massive volumes to find patterns, it often limits the causal conclusions that can be drawn. Using data farming, the work emphasizes purposeful data generation from simulation models, large-scale designed experiments, efficient exploration of input spaces, and explicit identification of cause-and-effect relationships.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/simulation-experiments-better-data-not-just-big-data-abstract/562612/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/simulation-experiments-better-data-not-just-big-data-abstract/562612.png","ImageObject",442,249,{"name":88,"@type":89},"Mafia Boss","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-21","2026-10-12",true,{"@type":98,"interactionType":99,"userInteractionCount":47},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"Why is big data not always sufficient for gaining insights?","Question",{"text":108,"@type":109},"The discussion notes that big data often relies on observational data, which limits the kinds of insights that can be drawn, especially regarding causal understanding.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"What does the “data farming” metaphor mean in the simulation context?",{"text":113,"@type":109},"Data farming refers to purposeful generation of data from simulation models, where large-scale designed experiments are used to systematically grow and analyze simulation outputs.",{"name":115,"@type":106,"acceptedAnswer":116},"What experimental concepts are highlighted for establishing causation?",{"text":117,"@type":109},"The text emphasizes control, randomization, and replication to manage bias and to support valid cause-and-effect reasoning in experimental design.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},562612,1792253995,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":47,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":15,"language":135,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":136,"faqs":137,"seo_title":138,"seo_description":61,"update_tm":139,"read_time":79},962090760730,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Proceedings of the 2014 Winter Simulation Conference  \nA. Tolk, S. D. Diallo, I. O. Ryzhov, L. Yilmaz, S. Buckley, and J. A. Miller, eds.  \nSIMULATION EXPERIMENTS: BETTER DATA, NOT JUST BIG DATA  \nSusan M. Sanchez  \nNaval Postgraduate School  \nOperations Research Department  \n1411 Cunningham Rd.  \nMonterey, CA 93943-5219, USA  \nABSTRACT  \nData 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.  \n1 INTRODUCTION  \nThere is no universally accepted definition for the term “big data.” Many have argued that we have always had big data, whenever we had more available than we could analyze in a timely fashion with existing tools. Running a single multiple regression in the 1940's was very difficult, since matrix multiplication and inversion for even a small problem (say, a handful of independent variables and a few hundred observations) is a non-trivial task when it must be done by hand. From that perspective, big data can be viewed as any data set that pushes against the limits of currently available technology.  \nWhat can be done with big data? Most people immediately think of data mining, a term that is ubiquitous in the literature. The concept of data farming is less well-known, but has been used in the defense community over the past decade. At the Simulation Experiments & Efficient Designs (SEED) Center for Data Farming at the Naval Postgraduate School, we describe the differences between these metaphors as follows.  \nMiners seek valuable nuggets of ore buried in the earth, but have no control over what's out there or how hard it is to extract the nuggets from their surroundings. As they take samples from the earth, they gather more information about the underlying geology. Similarly, data miners seek to uncover valuable nuggets of information buried within massive amounts of data. They may look at “all” the “big data”available at a particular point in time, but they typically have no control over what data are out there, nor how hard it will be to separate the useful information from the rest of the 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, by 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 advantage—but using large-scale designed experimentation. This allows them to learn more about the simulation model's behavior in a structured way. In this fashion, they “grow” data from their models, but  \n978-1-4799-7486-3/14/$31 .00 ©2014 IEEE 805  \nSanchez  \nin a manner that facilitates identifying the useful information. For large-scale simulation experiments, this often results in big data sets. Yet, although the data sets are big, they are far smaller than what would be needed to gain insights if the results were observational (i.e., obtained using ad hoc or","cbCaivLUzcOb4abo","https://ap.wps.com/l/cbCaivLUzcOb4abo","pdf",201666,"English","# Abstract\n# Introduction\n# Revisiting Causation and Correlation","[{\"question\":\"Why is big data not always sufficient for gaining insights?\",\"answer\":\"The discussion notes that big data often relies on observational data, which limits the kinds of insights that can be drawn, especially regarding causal understanding.\"},{\"question\":\"What does the “data farming” metaphor mean in the simulation context?\",\"answer\":\"Data farming refers to purposeful generation of data from simulation models, where large-scale designed experiments are used to systematically grow and analyze simulation outputs.\"},{\"question\":\"What experimental concepts are highlighted for establishing causation?\",\"answer\":\"The text emphasizes control, randomization, and replication to manage bias and to support valid cause-and-effect reasoning in experimental design.\"}]","SIMULATION EXPERIMENTS: BETTER DATA, NOT JUST BIG DATA - Abstract | PDF",1791809092]