Model-data ecosystems: Challenges, tools, and trends

Abstract

In the past few years, research around (big) data management has begun to intertwine with research around predictive modeling and simulation in novel and interesting ways. Driving this trend is an increasing recognition that information contained in real-world data must be combined with information from domain experts, as embodied in simulation models, in order to enable robust decision making under uncertainty. Simulation models of large, complex systems (traffic, biology, population well-being) consume and produce massive amounts of data and compound the challenges of traditional information management. We survey some challenges, mathematical tools, and future directions in the emerging research area of model-data ecosystems. Topics include (i) methods for enabling data-intensive simulation, (ii) simulation and information integration, and (iii) simulation metamodeling for guiding the generation of simulated data and the collection of real-world data. Copyright is held by the owner/author(s).