A data farmer's almanac

摘要

Summary form only given. 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.