This presentation was made by Mark Andrews at the NAFEMS eSeminar "Simulation & Digital Twins - Behind the Buzzwords" on the 2nd of May 2018.

As simulation, monitoring, and testing techniques continue to advance toward Digital Engineering ecosystem, many have found themselves drowning in Big Data. As the computational burden continues to increase, analysis becomes intractable. This presentation will focus on two methods that help manage Big Data: the subsampling of a large data set and the sliced sampling of a large data set into smaller, more manageable data sets for training emulators. Subsampling algorithms intelligently considers points for the subset that maintain the characteristics of the whole data set, reducing the potential bias is the subsampled data set. Similarly, sliced sampling intelligently partition the data into slices which allows for training a series of emulators that can be combined into one. A conceptual Digital Twin example will be given to illustrate the capabilities of each method.