Dynamic Applications is a paradigm whereby applications and measurements become a symbiotic feedback control system with the ability to dynamically incorporate additional data into an executing application and to dynamically steer the measurement process, which provides more accurate analysis and prediction, more precise controls, and more reliable outcomes.  

Big Data is a paradigm for methods to handle nearly infinite amounts of data that is either streamed or is historically stored in (potentially ever growing) datasets for data mining. Almost all interesting Dynamic Applications overlap with Big Data. Solving one solves for the other one, so it makes sense to study both simultaneously.  

The ability of an application to control and guide the measurement process and determine when, where, and how it is best to gather additional data has itself the potential of enabling more effective measurement methodologies. Furthermore, the incorporation of dynamic inputs into an executing application invokes new system modalities and helps create application software systems that can more accurately describe real world, complex systems. This enables the development of applications that intelligently adapt to evolving conditions and that infer new knowledge in ways that are not predetermined by the initialization parameters and initial static data.  

The need for such Dynamic Applications combined with Big Data has already emerged in business, engineering and scientific processes, analysis, and design. Manufacturing process controls, resource management, weather and climate prediction, traffic management, systems engineering, civil engineering, geological exploration, social and behavioral modeling, cognitive measurement, and bio-sensing are examples of areas likely to benefit from the combined approach. 

Dr. Craig Douglas is an SER Professor of Mathematics in the Department of Mathematics. He received his Ph.D. from Yale University. His research interests include simulating contaminant transport, wildland fires, combustion, and ocean circulation using dynamic data-driven techniques. He is best known for his work in multigrid methods. He has run MGNet, a repository for information related to multigrid, multilevel, multiscale, aggregation, defect correction, and domain decomposition methods, since its inception in 1991. Dr. Craig Douglas conducted the Dynamic Data-Driven Application System 2011 Workshop (DDDAS 2011) in conjunction with the International Conference on Computational Sciences 2011 (ICCS 2011) in Singapore.