Simulation experiments: Better data, not just big data

• Main Author: Sanchez, Susan M.
• Format: Conference Proceeding
• Language: English
• Subjects: Analytical models; Big data; Correlation; Data mining; Data models; Mathematical model; Numerical models
• ISSN: 0891-7736; 1558-4305
• DOI: 10.1109/WSC.2014.7019942

Data 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.