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.

Abstract: Vehicle tracking in adverse environments is a challenging problem because of the high number of factors constraining their motion and possibility of frequent occlusion. In such conditions, identification rates drop dramatically. Hyperspectral imaging is known to improve the robustness of target identification by recording extended data in many wavelengths. However, it is impossible to transmit such a high rate data in real time with a conventional full hyperspectral sensor. Thus, we present a persistent ground-based target tracking system, taking advantage of a state-of-the-art, adaptive, multi-modal sensor controlled by Dynamic Data Driven Applications Systems (DDDAS) methodology. This overcomes the data challenge of hyperspectral tracking by only using spectral data as required. Spectral features are inserted in a feature matching algorithm to identify spectrally likely matches and simplify multidimensional assignment algorithm. The sensor is tasked for spectra acquisition by the prior estimates from the Gaussian Sum Filter and foreground mask generated by the background subtraction. Prior information matching the target features is used to tackle false negatives in the background subtraction output. The proposed feature-aided tracking system is evaluated in a challenging scene with a realistic vehicular simulation.

Abstract: Human-machine interaction mixed initiatives require a pragmatic coordination between different systems. Context understanding is established from the content, analysis, and guidance from query-based coordination between users and machines. Inspired by Level 5 Information Fusion ‘user refinement’, a live-video computing (LVC) structure is presented for user-based query access of a data-base management of information. Information access includes multimedia fusion of query-based text, images, and exploited tracks which can be utilized for context assessment, content-based information retrieval (CBIR), and situation awareness. In this paper, we explore new developments in dynamic data-driven application systems (DDDAS) of context analysis for user support. Using a common image processing data set, a system-level time savings is demonstrated using a query-based approach in a context, control, and semantic-aware information fusion design

Abstract: Context understanding is established from the content, analysis, and guidance from query-based coordination between users and machines. In this manuscript, a live-video computing (LVC) approach is presented for access, comprehension and management of information for context assessment. Context assessment includes multimedia fusion of query-based text, images, and exploited tracks which can be utilized for image retrieval. In this paper, we explore the developments in database systems to enable context to be utilized in user-based queries for video tracking content extraction. Using a common image processing data set, we demonstrate activity analysis with context, privacy, and semantic-aware in a Dynamic Data-Driven Application System (DDDAS).

Abstract: A new dynamic data-driven application system (DDDAS) is proposed in this article to dynamically estimate a concentration plume and to plan optimal paths for unmanned aerial vehicles (UAVs) equipped with environmental sensors. The proposed DDDAS dynamically incorporates measured data from UAVs into an environmental simulation while simultaneously steering measurement processes. The main idea is to employ a few time-evolving proper orthogonal decomposition (POD) modes to simulate a coupled linear system, and to simultaneously measure plume concentration and plume source distribution via a reduced Kalman filter. In order to maximize the information gain, UAVs are dynamically driven to hot spots chosen based on the POD modes using a greedy algorithm. We demonstrate the efficacy of the data assimilation and control strategies in a numerical simulation and a field test.

Abstract: Distributed generation resources (DGs) and their utilization in large-scale power systems are attracting more and more utilities as they are becoming more qualitatively reliable and economically viable. However, uncertainties in power generation from DGs and fluctuations in load demand must be considered when determining the optimal operation plan for a microgrid. In this context, a novel dynamic data driven approach is proposed for determining the real-time operation plan of an electric microgrid while considering its conflicting objectives. In particular, the proposed approach is equipped with three modules: 1) a database including the real-time microgrid topology data (i.e., power demand, market price for electricity, etc.) and the data for environmental factors (i.e., solar radiation, wind speed, temperature, etc.); 2) a simulation, in which operation of the microgrid is simulated with embedded rule-based scale identification procedures; 3) a multi-objective optimization module which finds the near-optimal operation plan in terms of minimum operating cost and minimum emission using a particle-filtering based algorithm. The complexity of the optimization depends on the scale of the problem identified from the simulation module. The results obtained from the optimization module are sent back to the microgrid system to enhance its operation. The experiments conducted in this study have demonstrated the power of the proposed approach in real-time assessment and control of operation in microgrids.

Abstract: We consider the task of dynamic capability estimation for an unmanned aerial vehicle, which is needed to provide the vehicle with the ability to dynamically and autonomously sense, plan, and act in real time. Our dynamic data-driven application systems framework employs reduced models to achieve rapid evaluation runtimes. Our reduced models must also adapt to underlying dynamic system changes, such as changes due to structural damage or degradation of the system. Our dynamic reduced models take into account changes in the underlying system by directly learning from the data provided by sensors, without requiring access to the original high-fidelity model. We present here an adaptivity indicator that detects a change in the underlying system and so allows the initiation of the dynamic reduced modeling adaptation if necessary. The adaptivity indicator monitors the error of the dynamic reduced model by comparing model predictions with sensor data, and signals a change if the error exceeds a given threshold. The indicator is demonstrated on a deflection model of a damaged plate in bending. Local damage of the plate is modeled by a change in the thickness of the plate. The numerical results show that in this example the adaptivity indicator detects all changes in the thickness and correctly initiates the adaptation of the reduced model.

Abstract: In this paper, we introduce new methods for multiobjective, system-level optimization that have been incorporated into the Lightweight Dataflow for Dynamic Data Driven Application Systems (DDDAS) Environment (LiD4E). LiD4E is a design tool for optimized implementation of dynamic, data-driven stream mining systems using high-level dataflow models of computation. More specifically, we develop in this paper new methods for integrated modeling and optimization of real-time stream mining constraints, multidimensional stream mining performance (precision and recall), and energy efficiency. Using a design methodology centered on data-driven control of and coordination between alternative dataflow subsystems for stream mining (classification modes), we develop systematic methods for exploring complex, multidimensional design spaces associated with dynamic stream mining systems, and deriving sets of Pareto-optimal system configurations that can be switched among based on data characteristics and operating constraints.

Abstract: The DDDAS paradigm, unifying applications, mathematical modeling, and sensors, is now more relevant than ever with the advent of Large-Scale/Big-Data and Big-Computing. Large-Scale-Dynamic-Data (advertised as the next wave of Big Data) includes the integrated range of data from high-end systems and instruments together with the dynamic data arising from ubiquitous sensing and control in engineered, natural, and societal systems. In this paper we present Fresh Breeze, a dataflow-based execution and programming model and computer architecture and how it provides a sound basis to develop future computing systems that match the DDDAS challenges. The DDDAS' computation patterns and data storage needs are well matched by the Fresh Breeze system's codelet-based execution model and memory-chunk-based memory model, as well as the proposed ISA level architecture features to be highlighted in this paper. We have extended and improved a previous generation of Fresh Breeze simulation platform to model a Fresh Breeze processing chip comprising up to 64 processing cores with an ISA with new features to address the issues of efficient symbiotic processing, and have completed a compiler tool chain from an adapted version of the Java source language to machine-level codelets for the simulator. We have evaluated our current implementation on several standard kernels from linear algebra for which near-linear speedup versus the number of cores is achieved without manual parallelization or scale-specific performance tuning. These test kernels show effectiveness of the fine-grain task scheduling and load balancing features essential to achieving the best performance for DDDAS. It is expected that once planned support of stream computation and transaction processing is checked out, it will be possible to demonstrate superior performance for application codes of DDDAS.

Abstract: The deployment of networks of sensors and development of pertinent information processing techniques can facilitate the requirement of situational awareness present in many defense/surveillance systems. Sensors allow the collection and distributed processing of information in a variety of environments whose structure is not known and is dynamically changing with time. A distributed dynamic data driven (DDDAS-based) framework is developed in this paper to address distributed multi-threat tracking under limited sensor resources. The acquired sensor data will be used to control the sensing part of the sensor network, and utilize only the sensing devices that acquire good quality measurements about the present targets. The DDDAS-based concept will be utilized to enable efficient sensor activation of only those parts of the network located close to a target/object. A novel combination of stochastic filtering techniques, drift homotopy and sparsity-inducing canonical correlation analysis (S-CCA) is utilized to dynamically identify the target-informative sensors and utilize them to perform improved drift-based particle filtering techniques that will allow robust, stable and accurate distributed tracking of multiple objects. Numerical tests demonstrate the effectiveness of the novel framework.

Abstract: In software development, migration from a Data Base Management System (DBMS) to another, especially with distinct characteristics, is a challenge for programmers and database administrators. Changes in the application code in order to comply with new DBMS are usually vast, causing migrations infeasible. In order to tackle this problem, we present NoSQLayer, a framework capable to support conveniently migrating from relational (i.e., MySQL) to NoSQL DBMS (i.e., MongoDB). This framework is presented in two parts: (1) migration module; and, (2) mapping module. The first one is a set of methods enabling seamless migration between DBMSs (i.e. MySQL to MongoDB). The latter provides a persistence layer to process database requests, being capable to translate and execute these requests in any DBMS, returning the data in a suitable format as well. Experiments show NoSQLayer as a handful solution suitable to handle large volume of data (e.g., Web scale) in which traditional relational DBMS might be inept in the duty.

Abstract: The Bayesian Computational Sensor Network methodology is applied to small-scale structural health monitoring. A mobile robot equipped with vision and ultrasound sensor maps small-scale structures for damage localizes itself and the damage in the map. The combination of vision and ultrasound reduces the uncertainty in damage localization. The data storage and analysis takes place exploiting cloud computing mechanisms, and there is also an off-line computational model calibration component which returns information to the robot concerning updated on-board models as well as proposed sampling points. The approach is validated in a set of physical experiments.

Abstract: A triangulation of points in E^2, or a tetrahedronization of points in E^3, is used in many applications. It is not necessary to fulfill the Delaunay criteria in all cases. For large data (more then 5∙ 〖10〗^7 points), parallel methods are used for the purpose of decreasing run time. A new approach for fast, effective and highly parallel CPU and GPU triangulation, or tetrahedronization, of large data sets in E^2 or E^3 suitable for in core and out core memory processing, is proposed. Experimental results proved that the resulting triangulation/tetrahedralization, is close to the Delaunay triangulation/tetrahedralization. It also demonstrates the applicability of the method proposed in applications.

Abstract: Future cyber information systems are required to determine network performance including trust, resiliency, and timeliness. Using the Dynamic Data-Driven Application Systems (DDDAS) concepts; we develop a method for crisis management that incorporates sensed data, performance models, theoretical analysis, and service-based software. Using constructs from security and resiliency theories, the motivating concept is Resilient-DDDAS-as-a-Cloud Service (rDaaS). Service-based approaches allow a system to react as needed to the dynamics of the situation. The Resilient Cloud Middleware supports the analysis the data stored and retrieved in the cloud, management of processes, and coordination with the end user/application. The r-DaaS concept is demonstrated with a nuclear plant example for emergency response that demonstrates the importance of the DDDAS system level performance.

Abstract: Many Dynamic Data-Driven Application Systems (DDDAS) use replicated simulations to project possible future system states. In many cases there are substantial similarities among these different replications. In other cases output statistics are independent of certain simulation computations. This paper explores computational methods to exploit these properties to improve efficiency. We discuss a new algorithm to speed up the execution of replicated vehicle traffic simulations, where the output statistics of interest focus on one or more attributes such as the trajectory of a certain “target” vehicle. By focusing on correctly reproducing the behavior of the target vehicle and its interaction with other modeled entities across the different replications and modifying the event handling mechanism the execution time can be reduced on both serial and parallel machines. A speculative execution method using a tagging mechanism allows this speedup to occur without loss of accuracy in the output statistics.

Abstract: Stream processing (SP) became relevant mainly due to inexpensive and hence ubiquitous deployment of sensors in many domains (e.g., environmental monitoring, battle field monitoring). Other continuous data generators (web clicks, traffic data, network packets, mobile devices) have also prompted processing and analysis of these streams for applications such as traffic congestion/ accidents, network intrusion detection, and personalized marketing. Image processing has been researched for several decades. Recently there is emphasis on video stream analysis for situation monitoring due to the ubiquitous deployment of video cameras and unmanned aerial vehicles for security and other applications. This paper elaborates on the research and development issues that need to be addressed for extending the traditional stream processing framework for video analysis, especially for situation awareness. This entails extensions to: data model, operators and language for expressing complex situations, QoS specifications and algorithms needed for their satisfaction. Specifically, this paper demonstrates inadequacy of current data representation (e.g., relation and arrable) and querying capabilities to infer long-term research and development issues.