Abstract: This talk will provide an overview of new opportunities created by DDDAS (Dynamic Data Driven Applications Systems) and engendering a new vision for Exa-Scale computing and Big Data. Exa-Scale is considered the next frontier of high-end computational power and Big-Data seen as the the next generation of data-intensive. The presentation will discuss new opportunities that exist through DDDAS in synergism with a vision of additional dimensions to the Exa-Scale and Big Data, namely considering that the next wave of Big Data and Big Computing will result not only from the Exa-Scale frontiers but also from the emerging trend of “ubiquitous sensing” - ubiquitous instrumentation of systems by multitudes of distributed and heterogeneous collections of sets of sensors and controllers. Undeniably, achieving and exploiting Exa-Scale will enable larger scale simulations and complex “systems of systems” modeling, which will produce large sets of computed data contributing to the Big Data deluge, and adding to the data avalanche created by large scientific and engineering instruments. The emerging trend of large-scale, ubiquitous instrumentation through multitudes of sensors and controllers creates another dimension to computing and to data, whereby data and computations for processing and analyzing such data will be performed in combinations of collections of sensor and higher performance platforms – including the Exa-Scale. DDDAS provides a driver for such environments and an opportunity for new and advanced capabilities. The DDDAS paradigm, by its definition of dynamically integrating in a feed-back control loop the computational model with the instrumentation aspects of an application system, premises a unified computational-instrumentation platform supporting DDDAS environments. In general this unified computational-instrumentation platform will consist of a range of systems such as high-end (petascale, exascale), mid-range and personal computers and mobile devices, and instrumentation platforms such as large instruments or collections of sensors and controllers, such networks of large numbers of heterogeneous sensors and controllers. Otherwise stated, in DDDAS the computational and data environments of a given application span a range of platforms from the high-end computing to the data collection instruments - from the exa-scale to sensor-scale. Consequently, DDDAS environments present these kinds of unprecedented levels of computational resource heterogeneity and dynamicity which require new systems software to support the dynamic and adaptive runtime requirements of such environments. In addition to the role of DDDAS in unifying these two extremes of computing and data, there are also technological drivers that lead us to consider the extremes and the range of scales together. Thus far, conquering the exascale has been considered as having “unique” challenges in terms power efficiency requirements at the multicore unit level, dynamic management of the multitudes of such resources for optimized performance, fault tolerance and resilience, to new application algorithms. However, ubiquitous instrumentation environments comprising of sensors (and controllers) have corresponding requirements in terms of power efficiencies, fault tolerance, application algorithms dealing with sparse and incomplete data, etc. Moreover, it is quite possible that the same kinds of multicores that will populate exascale platforms will also be the building blocks of sensors and controllers. In fact, it is likely that these sensors and controllers – these new “killer micros” – they will drive the technologies at the device and chip levels. Leveraging common technologies for the range of platforms from the Exa-cale to the Sensor-Scale, not only is driven by the underlying technologies, but is also driven by the trends in the application requirements. Commonality in the building blocks (e.g. at the chip and multicore levels) across the range and the extremes of the computational and instrumentation platforms will simplify the challenges of supporting DDDAS environments. Such considerations create new opportunities for synergistically advancing and expediting advances in the two extreme scales of computing. The talk will address such challenges and opportunities in the context of projects pursuing capability advances through DDDAS such as those presented in the 2014 ICCCS/DDDAS Workshop and elsewhere.

Abstract: A framework for incorporating a swarm intelligent system with the Dynamic Data Driven Application System (DDDAS) is presented. Swarm intelligent systems, or artificial swarms, self-organize into useful emergent structures that are capable of solving complex problems, but are difficult to control and predict. The DDDAS concept utilizes repeated simulations of an executing application to improve analytic and predictive capability by creating a synergistic feedback loop. Incorporating DDDAS with swarm applications can significantly improve control of the swarm. An overview of the DDDAS framework for swarm control is presented, and then demonstrated with an example swarm application.

Abstract: A self-aware aerospace vehicle can dynamically adapt the way it performs missions by gathering information about itself and its surroundings and responding intelligently. We consider the specific challenge of an unmanned aerial vehicle that can dynamically and autonomously sense its structural state and re-plan its mission according to its estimated current structural health. The challenge is to achieve each of these tasks in real time---executing online models and exploiting dynamic data streams---while also accounting for uncertainty. Our approach combines information from physics-based models, simulated offline to build a scenario library, together with dynamic sensor data in order to estimate current flight capability. Our physics-based models analyze the system at both the local panel level and the global vehicle level.

Abstract: In this paper, we investigate new design methods for data-driven digital signal processing (DSP) systems that are targeted to resource- and energy-constrained embedded environments, such as UAVs, mobile communication platforms and wireless sensor networks. Signal processing applications, such as keyword matching, speaker identification, and face recognition, are of great importance in such environments. Due to critical application constraints on energy consumption, real-time performance, computational resources, and core application accuracy, the design spaces for such applications are highly complex. Thus, conventional static methods for configuring and executing such embedded DSP systems are severely limited in the degree to which processing tasks can adapt to current operating conditions and mission requirements. We address this limitation by developing a novel design framework for multi-mode, data driven signal processing systems, where different application modes with complementary trade-offs are selected, configured, executed, and switched dynamically, in a data-driven manner. We demonstrate the utility of our proposed new design methods on an energy-constrained, multi-mode face detection application.

Abstract: Tracking the movement of vehicles in urban environments using fixed position sensors, mobile sensors, and crowd-sourced data is a challenging but important problem in applications such as law enforcement and defense. A dynamic data driven application system (DDDAS) is described to track a vehicle’s movements by repeatedly identifying the vehicle under investigation from live image and video data, predict probable future locations of the vehicle, and reposition sensors or retarget requests for information, in order to reacquire the vehicle under surveillance. An overview of the system is described that includes image processing algorithms to detect and recapture the vehicle from live image data, a computational framework to predict probable vehicle locations at future points in time, and an information and power aware data distribution system to disseminate data and requests for information. A prototype of the envisioned system is described that is under development in the midtown area of Atlanta, Georgia in the United States.

Abstract: Southern European countries are severely affected by forest fires every year, which lead to very large environmental damages and great economic investments to recover affected areas. All affected countries invest lots of resources to minimize fire damages. Emerging technologies are used to help wildfire analysts determine fire behavior and spread aiming at a more efficient use of resources in fire fighting. In the case of trans-boundary fires, the European Forest Fire Information System (EFFIS) works as a complementary system to national and regional systems in the countries, providing information required for international collaboration on forest fire prevention and fighting. In this work, we describe a way of exploiting all the available information in the system to feed a dynamic data driven wildfire behavior prediction model that can deliver results to support operational decisions. The model is able to calibrate the unknown parameters based on the real observed data, such as wind condition and fuel moistures, using a steering loop. Since this process is computational intensive, we exploit multi-core platforms using a hybrid MPI-OpenMP programming paradigm.

Abstract: In this paper we present new ideas to greatly enhance the quality of uncertainty quantification in the DDDAS framework. We build on ongoing work in large scale transport of geophysical mass of volcanic origin -- a danger to both land based installations and airborne vehicles.

Abstract: We present a dynamic data-driven decision support for aquaculture farm closure. In decision support, we use machine learning techniques in predicting closures of a shellfish farm. As environmental time series are used in closure, we propose two approaches using time series and machine learning for closure prediction. In one approach, we consider time series prediction and then using expert rules to predict closure. In other approach, we use time series classification for closure prediction. Both approaches exploit a dynamic data-driven technique where prediction models are updated with the update of new data to predict closure decisions. Experimental results at a case study shellfish farm validate the applicability of the proposed method in aquaculture decision support.

Abstract: In this paper, we outline key features that dynamic data-driven application systems (DDDAS) have. The term Big Data (BD) has come into being in recent years that is highly applicable to most DDDAS since most applications use networks of sensors that generate an overwhelming amount of data in the lifespan of the application runs. We describe what a dynamic big-data-driven application system (DBDDAS) toolkit must have in order to provide all of the essential building blocks that are necessary to easily create new DDDAS without re-inventing the building blocks.

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. Errors in measurements and uncertainties in the model lead to inaccurate inference results. This work develops a methodology to estimate the impact of various errors on the variational solution of a DDDAS inference problem. The methodology is based on models described by ordinary differential equations, and use first-order and second-order adjoint methodologies. Numerical experiments with the heat equation illustrate the use of the proposed error estimation machinery.

Abstract: Many inference problems in environmental DDDAS must contend with high dimensional models and non-Gaussian uncertainties, including but not limited to Data Assimilation, Targeting and Planning. In this this paper, we present the Mixture Ensemble Filter (MEnF) which extends ensemble filtering to non-Gaussian inference using Gaussian mixtures. In contrast to the state of the art, MEnF embodies an exact update equation that neither requires explicit calculation of mixture element moments nor ad-hoc association rules between ensemble members and mixture elements. MEnF is applied to the chaotic Lorenz-63 model and to a chaotic soliton model that allows idealized and systematic studies of localized phenomena. In both cases, MEnF outperforms contemporary approaches, and replaces ad-hoc Gaussian Mixture approaches for non-Gaussian inference.

Abstract: We present a formulation, solution method, and program acceleration techniques for two dynamic control scenarios, both with the common goal of optimizing resource allocations. These approaches allocate resources in a non-myopic way, accounting for long-term impacts of current control decisions via nominal belief-state optimization (NBO). In both scenarios, the solution techniques are parallelized for reduced execution time. A novel aspect is included in the second scenario: dynamically allocating the computational resources in an online fashion which is made possible through constant aspect ratio tiling (CART).

Abstract: The dataflow programming paradigm shows an important way to improve the programming productivity for domain experts. In this position paper we propose COStream,a programming language that is based on synchronization dataflow execution model for application. We also propose a compiler framework for COStream on multi-core architecture. In the compiler, we use an inter-thread software pipelining schedule to exploit the parallelism among the cores. We implement the COStream compiler framework on x86 multi-core architecture and perform the experiments to evaluate the system.

Abstract: Information fusion includes signals, features, and decision-level analysis over various types of data including imagery, text, and cyber security detection. With the maturity of data processing, the explosion of big data, and the need for user acceptance; the Dynamic Data-Driven Application System (DDDAS) philosophy fosters insights into the usability of information systems solutions. In this paper, we explore a notion of an adaptive adjustment of secure communication trust analysis that seeks a balance between standard static solutions versus dynamic-data driven updates. A use case is provided in determining trust for a cyber security scenario exploring comparisons of Bayesian versus evidential reasoning for dynamic security detection updates. Using the evidential reasoning proportional conflict redistribution (PCR) method, we demonstrate improved trust for dynamically changing detections of denial of service attacks.

Abstract: To realize the full potential of mobile crowd sensing, techniques are needed to deal with uncertainty in participant locations and trajectories. We propose a novel model for spatial task assignment in mobile crowd sensing that uses a dynamic and adaptive data driven scheme to assign moving participants with uncertain trajectories to sensing tasks, in a near-optimal manner. Our scheme is based on building a mobility model from publicly available trajectory history and estimating posterior location values using noisy/uncertain measurements upon which initial tasking assignments are made. These assignments may be refined locally (using exact information) and used by participants to steer their future data collection, which completes the feedback loop. We present the design of our proposed approach with rationale to suggest its value in effective mobile crowd sensing task assignment in the presence of uncertain trajectories.

Abstract: This work aims to mathematically formalize the notion of context, with the purpose of allowing contextual decision-making in order to improve performance in dynamic data driven classification systems. We present definitions for both intrinsic context, i.e. factors which directly affect sensor measurements for a given event, as well as extrinsic context, i.e. factors which do not affect the sensor measurements directly, but do affect the interpretation of collected data. Supervised and unsupervised modeling techniques to derive context and context labels from sensor data are formulated. Here, supervised modeling incorporates the a priori known factors affecting the sensing modalities, while unsupervised modeling autonomously discovers the structure of those factors in sensor data. Context-aware event classification algorithms are developed by adapting the classification boundaries, dependent on the current operational context. Improvements in context-aware classification have been quantified and validated in an unattended sensor-fence application for US Border Monitoring. Field data, collected with seismic sensors on different ground types, are analyzed in order to classify two types of walking across the border, namely, normal and stealthy. The classification is shown to be strongly dependent on the context (specifically, soil type: gravel or moist soil).