Abstract: This talk will provide an overview of future directions in Big Data and Big Computing, as driven by the DDDAS paradigm. In DDDAS, the computation and instrumentation aspects of an application system are integrated in a dynamic feed-back control loop. Thus, by its inception DDDAS is a driver for application support environments where the computational platforms span the diverse range of high-end and mid-range computing and including the instrumentation platforms, stationary and mobile networked sensors, and end-user devices. Commensurately, the data involved in DDDAS environments span data associated with complex computer models of application systems to instrumentation-data – either collected from large instruments or from the multitudes of heterogeneous mobile and stationary ubiquitous sensing devices, and including end-user devices. Data from ubiquitous sensing constitute the next wave of Big Data. These collections of ubiquitous and heterogeneous sensing devices not only are sources of large volumes of heterogeneous sets of data, but also the amount of computing that is performed collectively on the multitudes of instrumentation/sensor platforms amounts to significant computational power which should be viewed in tandem with that performed in the high-end platforms. In DDDAS environments, this range of platforms - from the high-end, to the instrumentation and end-user platforms - constitute the dynamically integrated, unified platform referred to here as Large-Scale-Big-Computing (LSBC); the diverse sets of data - from high-end computing data to data from large sets of heterogeneous sensing are referred to as Large-Scale-Big-Data (LSBD). There are challenges and opportunities in supporting and exploiting Large-Scale-Big-Computing and Large-Scale-Big-Data. DDDAS has been applied and is creating new capabilities in many application areas spanning systems from the nanoscale to the terra-scale and the extraterra-scale, and covering a multitude of domains such as: physical, chemical, biological systems (e.g.: engineered materials, protein folding, bionetworks); engineered systems (e.g.: structural health monitoring, decision support and environment cognizant operation); surveillance, co-operative sensing, autonomic coordination, cognition; energy efficient operations; medical and health systems (e.g.: MRI imaging, seizure control); ecological and environmental systems (e.g.: earthquakes, hurricanes, tornados, wildfires, volcanic eruptions and ash transport, chemical pollution); fault tolerant critical infrastructure systems (e.g.: electric-powergrids, transportation systems); manufacturing processes planning and control; space weather and adverse atmospheric events; cybersecurity; systems software. DDDAS is a driver for LSBC and LSBD environments and but also a methodology to efficiently manage and exploit these large-scale-heterogeneous resources, aspects which will be addressed in the talk; additional examples of DDDAS-based new capabilities for such applications are provided in other papers in this workshop.

Abstract: In autonomous mapping of geophysical fluids, a DDDAS framework involves reduced models constructed offline for online use. Here we show that classical model reduction is ill-suited to deal with model errors manifest in coherent fluids as feature errors including position, scale, shape or other deformations. New fluid representations are required. We propose augmenting amplitude vector spaces by non-parametric deformation vector fields which enables the synthesis of new Principal Appearance and Geometry modes, Coherent Random Field expansions, and an Adaptive Reduced Order Model by Alignment (AROMA) framework. AROMA dynamically deforms reduced models in response to feature errors. It provides robustness and efficiency in inference by unifying perceptual and physical representations of coherent fluids that to the best of our knowledge has not hitherto been proposed.

Abstract: Inference problems in dynamically data-driven application systems use physical measurements along with a physical model to estimate the parameters or state of a physical system. Developing parallel algorithms to solve inference problems can improve the process of estimating and predicting the physical state of a system. Solution to inference problems using the variational approach require multiple evaluations of the associated cost function and gradient, where the gradient is defined as the increase/decrease inflection point of the variable between two points. In this paper we present a scalable algorithm based on augmented Lagrangian approach to solve the variational inference problem. The augmented Lagrangian framework facilitates parallel cost function and gradient computations. We show that the methodology is highly scalable with increasing problem size by applying it for the Lorenz-96 model.

Abstract: The omnipresent pervasiveness of personal devices will expand the applicability of the DDDAS paradigm in innumerable ways. While every single smartphone or wearable device is potentially a sensor with powerful computing and data capabilities, privacy and security in the context of human participants must be addressed to leverage the infinite possibilities of dynamic data driven application systems. We propose a security and privacy preserving framework for next generation systems that harness the full power of the DDDAS paradigm while (1) ensuring provable privacy guarantees for sensitive data; (2) enabling field-level, intermediate, and central hierarchical feedback-driven analysis for both data volume mitigation and security; and (3)intrinsically addressing uncertainty caused either by measurement error or security-driven data perturbation. These thrusts will form the foundation for secure and private deployments of large scale hybrid participant-sensor DDDAS systems of the future.