HANDBOOK OF DYNAMIC DATA DRIVEN APPLICATIONS SYSTEMS : volume 1

The Handbook of Dynamic Data Driven Applications Systems establishes an authoritative reference of DDDAS, pioneered by Dr. Darema and the co-authors for researchers and practitioners developing DDDAS technologies. Beginning with general concepts and history of the paradigm, the text provides 32 chapters by leading experts in ten application areas to enable an accurate understanding, analysis, and control of complex systems; be they natural, engineered, or societal: The authors explain how DDDAS unifies the computational and instrumentation aspects of an application system, extends the notion of Smart Computing to span from the high-end to the real-time data acquisition and control, and manages Big Data exploitation with high-dimensional model coordination. The Dynamically Data Driven Applications Systems (DDDAS) paradigm inspired research regarding the prediction of severe storms. Specifically, the DDDAS concept allows atmospheric observing systems, computer forecast models, and cyberinfrastructure to dynamically configure themselves in optimal ways in direct response to current or anticipated weather conditions. In so doing, all resources are used in an optimal manner to maximize the quality and timeliness of information they provide. Kelvin Droegemeier, Regents’ Professor of Meteorology at the University of Oklahoma; former Director of the White House Office of Science and Technology Policy We may well be entering the golden age of data science, as society in general has come to appreciate the possibilities for organizational strategies that harness massive streams of data. The challenges and opportunities are even greater when the data or the underlying system are dynamic - and DDDAS is the time-tested paradigm for realizing this potential. Sangtae Kim, Distinguished Professor of Mechanical Engineering and Distinguished Professor of Chemical Engineering at Purdue University Contents About the Editors 1 Introduction to the Dynamic Data Driven Applications Systems (DDDAS) Paradigm 1.1 Introduction 1.2 What Is DDDAS? 1.3 State Estimation and Data Assimilation 1.3.1 DDDAS and Adaptive State Estimation 1.3.2 Does DDDAS Use Feedback Control? 1.4 DDDAS Methods 1.5 DDDAS Research Areas of Historical Development 1.5.1 Theory: Modeling and Analysis 1.5.2 Methods: Domain Applications 1.5.3 Analysis and Design: Systems and Architectures 1.6 Book Overview 1.7 DDDAS Future 1.8 Summary References Part I Measurement-Aware: Data Assimilation, Uncertainty Quantification 2 Tractable Non-Gaussian Representations in Dynamic Data Driven Coherent Fluid Mapping 2.1 Introduction 2.1.1 Systems Dynamics and Optimization 2.1.2 Dynamically Deformable Reduced Models 2.1.3 Nonlinear High Dimensional Inference 2.2 Ensemble Learning in Mixture Ensembles 2.2.1 Mixture Ensemble Filter and Smoother 2.3 Nonlinear Filtering Must Reduce Total Variance 2.4 Ensemble Learning with a Stacked Cascade 2.4.1 Application Example 2.5 Information Theoretic Learning in Filtering 2.5.1 Tractable Information Theoretic Approach 2.6 Application Example 2.7 Conclusions References 3 Dynamic Data-Driven Adaptive Observations in Data Assimilation for Multi-scale Systems 3.1 Introduction 3.2 Dimensional Reduction and Homogenization 3.3 Data Assimilation in Multi-scale Systems 3.4 Information-Theoretic Sensor Selection Strategy 3.4.1 The Linear Case 3.4.2 Information Flow for the Coarse Grained Dynamics 3.4.3 Finite-Time Lyapunov Exponents and Singular Vectors 3.4.4 Sensor Selection and the Lorenz 1963 Model 3.4.4.1 Sensor Selection with Kullback-Leibler Divergence 3.4.4.2 Sensor Selection with Singular Vectors 3.4.4.3 Influence of Singular Values in Discrete-Time, Linear Gaussian Case 3.4.4.4 Numerical Results 3.5 Conclusions References 4 Dynamic Data-Driven Uncertainty Quantification via Polynomial Chaos for Space Situational Awareness 4.1 Introduction 4.2 Gaussian Mixture Models 4.3 Polynomial Chaos 4.4 Polynomial Chaos with Gaussian Mixture Models 4.5 Global Ionosphere-Thermosphere Model 4.6 Results 4.6.1 Orbital Uncertainty Quantification 4.6.2 Initial Results for Atmospheric Density Forecasting 4.7 Conclusion References Part II Signals-Aware: Process Monitoring 5 Towards Learning Spatio-Temporal Data Stream Relationships for Failure Detection in Avionics 5.1 Introduction 5.2 Background 5.2.1 Error Detection and Correction Methods 5.2.2 Spatio-Temporal Data Stream Processing System 5.3 Design of Machine Learning Component 5.3.1 Prediction in PILOTS Programming Language 5.3.2 Prediction in PILOTS Runtime 5.4 Data-Driven Learning of Linear Models 5.4.1 Learning Algorithm 5.4.2 Linear Model Accuracy 5.5 Statistical Learning of Dynamic Models 5.5.1 Offline Supervised Learning 5.5.1.1 Gaussian Naïve Bayes Classifiers 5.5.1.2 Offline Learning Phase 5.5.2 Dynamic Online Unsupervised Learning 5.5.2.1 Major and Minor Modes 5.5.2.2 Online Learning Phase 5.6 Case Study: Airplane Weight Estimation 5.6.1 Experimental Settings 5.6.1.1 Data Generation 5.6.1.2 Implementation and Evaluation of Learning Algorithms 5.6.2 Aerodynamic Model Parameter Estimation by Linear Regression 5.6.2.1 Assumption 5.6.2.2 Linear Regression Model 5.6.3 Error Detection and Correction Using Error Signatures 5.6.3.1 PILOTS Program 5.6.3.2 Error Detection 5.6.3.3 Software Parameter Settings 5.6.3.4 Results 5.6.4 Error Detection Using the Dynamic Bayes Classifier 5.6.4.1 PILOTS Program 5.6.4.2 Mode Prediction Evaluation 5.6.4.3 Experimental Settings 5.6.4.4 Results 5.6.5 Comparison Between Error Signatures and Dynamic Bayes Classifier 5.7 Related Work 5.8 Discussion and Future Work References 6 Markov Modeling via Spectral Analysis: Application to Detecting Combustion Instabilities 6.1 Motivation and Introduction 6.2 Background and Mathematical Preliminaries 6.3 Proposed Approach 6.3.1 Estimation of Reduced-Order Markov Model 6.3.2 Estimation of Parameters for the Reduced-Order Markov Model 6.3.3 Pseudocode of the Main Algorithm 6.4 Combustion Experiment Details 6.5 Results and Discussion 6.6 Conclusions and Future Work References 7 Dynamic Space-Time Model for Syndromic Surveillance with Particle Filters and Dirichlet Process 7.1 Background 7.2 Methodology 7.2.1 Dirichlet Process 7.2.2 Particle Filter 7.2.3 Evaluation of the Method 7.3 Applications with Indiana Surveillance Data 7.4 Conclusions and Future Work References Part III Structures-Aware: Health Modeling 8 A Computational Steering Framework for Large-Scale Composite Structures: Part I—Parametric-Based Design and Analysis 8.1 Introduction 8.2 Elements of the DISCERN Framework 8.2.1 Parametric-Based Design and Interactive Visual Programming 8.2.2 Analysis 8.2.2.1 Modeling of Thin-Shell Composites Using Isogeometric Analysis 8.2.2.2 The Structural Health Monitoring (SHM) System 8.3 NREL Phase VI Wind Turbine Blade 8.3.1 Setting Material Properties, Loads, and Boundary Conditions 8.3.2 Simulation Results 8.3.3 Visualization of IGA Results 8.3.4 Parametric Design Modification 8.4 Conclusions References 9 Development of Intelligent and Predictive Self-Healing Composite Structures Using Dynamic Data-Driven Applications Systems 9.1 Introduction 9.1.1 Overview of the Proposed Intelligent Structure 9.2 Experimental Section 9.2.1 Double-Cantilever Beam (DCB) Test Specimen Fabrication 9.2.1.1 Materials 9.2.2 Manufacturing of DCB Test Specimens 9.2.3 Fracture and Healing Protocols 9.2.4 Fracture Analysis 9.3 Results and Discussions 9.3.1 Fracture Test Results 9.3.2 Quantification of Healing Efficiency 9.3.3 Fractography Using Scanning Electron Microscopy (SEM) 9.3.4 Parametric Sensitivity Analysis 9.4 Concluding Remarks References 10 Dynamic Data-Driven Approach for Unmanned Aircraft Systems Aero-elastic Response Analysis 10.1 Introduction 10.2 Framework 10.3 Aero-Elastic Simulation 10.4 Data-Driven Prediction Framework 10.5 Case Study 10.6 Decision Support 10.7 Concluding Remarks References Part IV Environment-Aware: Earth, Biological, and Space Systems 11 Transforming Wildfire Detection and Prediction Using New and Underused Sensor and Data Sources Integrated withModeling 11.1 Introduction 11.2 Background 11.2.1 Forecasting Approaches 11.2.2 The 2015 Canyon Creek Wildfire Complex 11.3 Methods 11.3.1 Wildland Fire Detection, Mapping, and Monitoring 11.3.1.1 The Visible and Infrared Imaging Radiometer Suite (VIIRS) 11.3.1.2 Landsat 11.3.2 Coupled Weather-Wildland Fire Modeling 11.4 Experiment Design and Results 11.4.1 Dynamic Data Driven Model Invocation 11.4.2 Results: Impact on Fire Detection 11.4.3 Results: Impact on Fire Prediction 11.4.4 Integrated Results 11.5 Discussion 11.6 Conclusions References 12 Dynamic Data Driven Application Systems for Identification of Biomarkers in DNA Methylation 12.1 Introduction 12.2 DNA Methylation Data 12.3 Proposed DDDAS-Based Learning Framework (3D-HCL) 12.3.1 Initialization Algorithm: Principal Component Analysis 12.3.2 Clustering Algorithm: Hierarchical Clustering 12.3.3 Orchestration Procedure: Cluster Membership Score Based Algorithm 12.3.4 Outlier Detection Algorithm 12.3.5 Dimension Reduction Algorithm: Locus Information Score Based Algorithm 12.4 Results and Discussion 12.4.1 Learning from Training Data 12.4.2 Learning from Test Data 12.5 Conclusion References 13 Photometric Stereopsis for 3D Reconstruction of Space Objects 13.1 Introduction 13.2 Problem Statement and Background 13.3 Photometric Stereo 13.3.1 Formulation 13.3.2 Modified Photometric Stereo 13.3.3 Surface Reconstruction and Depth Estimation 13.4 Photometric stereo In Motion 13.5 Covariance Analysis 13.5.1 Raw Sensor Noise and the Intensity Uncertainty 13.5.2 Covariance of the Normal Vector Estimates 13.5.3 Error Covariance of The Surface Points 13.6 Simulation and Experiment 13.6.1 Stationary Observation of Lambertian Surface 13.6.2 Observation of Lambertian Surface From Non-Stationary View Point 13.6.3 Observation of Non-Lambertian Surface From Non-Stationary View Point 13.7 Conclusion References Part V Situation Aware: Tracking Methods 14 Aided Optimal Search: Data-Driven Target Pursuit from On-Demand Delayed Binary Observations 14.1 Introduction 14.2 Related Work 14.3 Notation and Preliminaries 14.4 Problem Statement 14.5 Target Trajectory Estimation Via Sparse Gaussian Mixture Model 14.5.1 Prediction 14.5.2 Update 14.6 Ground-Sensor-Aided Search Via Mixed-Integer Convex Programming 14.6.1 Ground-Sensor-Aided Optimal Search 14.6.2 Sampling-Based Aided Search via Convex Mixed-Integer Programming 14.7 Numerical Experiments 14.7.1 The Scenario 14.7.2 The Algorithms: Implementation Details 14.7.3 A Typical Result 14.7.4 Monte Carlo Analysis 14.8 Conclusion A Prediction Equations B Update Equations References 15 Optimization of Multi-target Tracking Within a Sensor Network Via Information Guided Clustering 15.1 Introduction 15.2 Target Tracking and Motivating Problem 15.2.1 Problem Formulation 15.2.1.1 Prediction 15.2.1.2 Update 15.2.2 Motivation: The Euclidean Cluster 15.3 Information Guided Rapid Clustering Algorithm 15.3.1 Sensing Feasibility 15.3.1.1 A Note on Feasibility 15.3.2 Information Utility 15.3.2.1 Mahalanobis Distance 15.3.2.2 Sensing Mode and Quality Cluster Creation 15.3.3 Communication Cost 15.3.4 Final Optimal Sensor Selection 15.3.4.1 A Note on the Use of Two Separate Information Gain Metrics 15.3.5 Procedure of the IGRCA 15.4 Target Dynamics and Sensor Measurement Models 15.4.1 System Model 15.4.2 Measurement Model 15.5 Analysis 15.5.1 Frame of Reference 15.5.2 Extension to Bearing Sensors 15.6 Simulation Results 15.6.1 Performance Metrics 15.6.2 Algorithm Win Probability: Simulation 15.6.3 Multiple Target Tracking 15.6.3.1 Comparative Posterior CRLB: Various Sensor Densities 15.6.3.2 Computational Expenditure 15.6.3.3 Comparative Posterior CRLB: Various Number of Targets 15.7 Conclusion References 16 Data-Driven Prediction of Confidence for EVAR in Time-Varying Datasets 16.1 Introduction 16.2 Preliminaries 16.2.1 Probabilistic Inequalities 16.2.2 Lévy Process 16.2.3 Information Gain and Exploration 16.2.4 Entropic Value at Risk (EVAR) Risk Measure 16.3 Formulation: Exploration as Multi-play N-Armed Restless Bandits 16.3.1 Multi-play N-Armed Restless Bandit Formulation 16.3.2 Data-Driven EVAR 16.3.2.1 Requirement for Predicting Data-Driven EVAR 16.3.3 Modeling the Information Gain 16.3.3.1 Poisson Exposure Distribution (Ped) Likelihood 16.3.3.2 Poisson Exposure Process (Pep) Model 16.4 Algorithms and Probabilistic Guarantees 16.4.1 The Time-Invariant Case: Ped-Based Exploration 16.4.2 The Time-Varying Case: Pep-Based Exploration 16.5 Experimental Evaluation 16.5.1 Evaluation Setup 16.5.2 Assumptions on Prior and Posterior Distributions 16.5.3 EVAR-Seeking Algorithms and Results 16.6 Conclusion References Part VI Context-Aware: Coordinated Control 17 DDDAS for Attack Detection and Isolation of Control Systems 17.1 Introduction 17.2 Problem Formulation 17.2.1 Cyber-Attacks in Control Systems 17.3 DDDAS Anomaly Isolation and Response 17.3.1 Anomaly Detection 17.3.2 Anomaly Isolation 17.4 Obtaining a Simulation Model 17.5 Case Study 17.5.1 Description of the System 17.5.2 Obtaining the Model System from I/O Data 17.5.3 Detection of Sensor Attacks 17.5.4 Isolation of Sensor Attacks 17.6 Conclusions and Future Work References 18 Approximate Local Utility Design for Potential Game Approach to Cooperative Sensor Network Planning 18.1 Introduction 18.2 Background 18.2.1 Information Measures 18.2.1.1 Entropy 18.2.1.2 Mutual Information 18.2.2 Game-Theoretic Architecture 18.2.2.1 Strategic Form Game 18.2.2.2 Potential Game 18.3 Sensor Network Planning as a Potential Game 18.3.1 Cooperative Sensor Planning for Maximum Information 18.3.2 Sensor Selection as Potential Game 18.4 Approximate Local Utility Design 18.4.1 Neighbors with Correlation 18.4.2 Determination of the Neighbor Set 18.4.3 Computation Time Analysis 18.5 Numerical Example 18.5.1 Sensor Targeting for Weather Forecast 18.5.2 Comparative Results 18.6 Conclusion References 19 Dynamic Sensor-Actor Interactions for Path-Planning in a Threat Field 19.1 Introduction 19.1.1 Literature Review 19.1.2 Proposed Work and Contributions 19.2 Problem Formulation 19.3 Actor-Driven Sensor Reconfiguration 19.4 Results and Discussion of Numerical Simulation Experiments 19.5 Conclusions References Part VII Energy-Aware: Power Systems 20 Energy-Aware Dynamic Data-Driven Distributed Traffic Simulation for Energy and Emissions Reduction 20.1 Introduction 20.2 Research Components: Models and Architecture 20.2.1 Cellular Automata Modeling 20.2.1.1 Methodology 20.2.2 Bayesian Inference Approach 20.3 Energy and Emission Modeling with MOVES-Matrix 20.4 Distributed Simulation Middleware 20.4.1 G-RTI Architecture 20.4.2 Energy Consumption Measurements 20.5 Concluding Comments and Future Work References 21 A Dynamic Data-Driven Optimization Framework for Demand Side Management in Microgrids 21.1 Introduction 21.2 Proposed Framework 21.2.1 Simulation Module 21.2.2 Optimization Module 21.2.3 ε-Constraint Method for Multi-objective Optimization 21.2.4 Real-Time Decision Making Module 21.3 Experiments and Results 21.4 Conclusion References 22 Dynamic Data Driven Partitioning of Smart Grid for Improving Power Efficiency by Combinining K-Means and Fuzzy Methods 22.1 Introduction 22.2 Methodology 22.2.1 k-means Partitioning Algorithm 22.2.2 Fuzzy Logic Decision Making Model 22.3 Simulations Configuration 22.4 Simulation Results 22.5 Conclusion References Part VIII Process-Aware: Image and Video Coding 23 Design of a Dynamic Data-Driven System for Multispectral Video Processing 23.1 Introduction 23.2 Related Work 23.3 Lightweight Dataflow (LD) Spectral 23.4 Run-Time System Model 23.5 Band Subset Processing 23.6 Band Subset Selection 23.7 Experimental Results 23.7.1 Experimental Setup 23.7.2 Accuracy Metric 23.7.3 Example Images 23.7.4 Accuracy Evaluation 23.7.5 Execution Time Evaluation 23.8 Conclusions References 24 Light Field and Plenoptic Point Cloud Compression 24.1 Lenslet-Based Light Field Image Compression 24.1.1 Self-similarity-Based Light Field Image Compression 24.1.2 Pseudo-Sequence-Based Light Field Image Compression 24.1.2.1 The 2-D Hierarchical Coding Structure 24.1.2.2 Distance-Based Reference Frame Selection and Motion Vector Scaling 24.1.3 Dictionary-Learning-Based Light Field Image Compression 24.2 Camera-Array-Based Light Field Image Compression 24.2.1 Compression of Dense-Camera-Array-Based Light Field Images with Obvious Perspective Motions 24.2.1.1 The 2-D Hierarchical Coding Structure 24.2.1.2 Global Perspective Model 24.2.1.3 Local Four-Parameter Affine Motion Model 24.2.2 Compression of Dense-Camera-Array-Based Light Field Images with Translational Motions 24.3 Surface Light Field Image Compression 24.3.1 Interpolation-Based Surface Light Field Image Compression 24.3.2 Plenoptic Point Cloud Compression 24.4 Conclusion References 25 On Compression of Machine-Derived Context Sets for Fusion of Multi-modal Sensor Data 25.1 Introduction 25.2 Learning Context from Data 25.3 Cardinality Reduction of Context Sets 25.3.1 Graph-Theoretic Compression 25.3.2 Compression by Subset Selection 25.4 Experiments and Results 25.5 Conclusion References Part IX Cyber-Aware: Security and Computing 26 Simulation-Based Optimization as a Service for Dynamic Data-Driven Applications Systems 26.1 Introduction 26.2 Problem Statement and Overview of SBOaaS 26.2.1 Motivating Case Study: Dynamic Traffic Light Control System 26.2.2 DDDAS-Specific Problem Statement and the SBOaaS Approach 26.2.3 Key Features of SBOaaS 26.3 Anytime Optimization Using Parallel Greedy Algorithm 26.3.1 Coordinate Greedy 26.3.2 K-Coordinate Greedy 26.3.3 Adaptive K-Coordinate Greedy 26.4 System Architecture 26.4.1 Runtime Architecture 26.4.2 Design Time Architecture 26.4.3 User Interaction Framework 26.5 Evaluation 26.5.1 Online Simulation-Based Optimization for Dynamic Traffic Light Control System 26.5.1.1 Environment 26.5.1.2 Experiment 1 26.5.1.3 Results 26.5.1.4 Experiment 2 26.5.1.5 Results 26.5.2 System Evaluation 26.5.2.1 Result 26.6 Related Work 26.6.1 Coordinate Greedy Algorithm 26.6.2 Cloud-Based Simulation Service 26.6.3 Traffic Light Optimal Control Problem 26.7 Conclusions References 27 Privacy and Security Issues in DDDAS Systems 27.1 Introduction 27.2 Background 27.3 Overview and Goals 27.4 Conceptual PREDICT Model 27.4.1 System Model 27.4.2 Privacy Model 27.5 PREDICT Framework: Technical Approaches and Results 27.5.1 Privacy Preserving Data Collection and Data Aggregation with Feedback Control 27.5.2 Dynamic Data Modeling with Uncertainty Quantification 27.5.3 Secure Data Aggregation and Feedback Control Without Trusted Aggregator 27.6 Conclusion References 28 Multimedia Content Analysis with Dynamic Data Driven Applications Systems (DDDAS) 28.1 Introduction 28.2 Multimedia Analysis 28.2.1 QuEST 28.2.2 Unexpected Query 28.3 Multimedia Contextual Reality 28.4 Modeling for Multimedia Content 28.4.1 Data Oriented Models (Cyber) 28.4.2 Analytical Models (Physical) 28.4.3 Cyber-Physical Models 28.5 Results: Activity Analysis 28.5.1 Interface 28.5.2 Case 1: Intersection 28.5.3 Case 2: Parking Lot 28.6 Conclusions References Part X Systems-Aware: Design Methods 29 Parzen Windows: Simplest Regularization Algorithm 29.1 Introduction 29.2 Related Work 29.3 Regularized Least Squares Method 29.4 Approximate Regularized Least Squares 29.5 Error Bound for SR 29.6 Bias, Variance and Regularization Constant 29.6.1 Regularization Constant 29.6.2 Regularization Constant and Simplest Regularization 29.7 Computational Complexity 29.8 SR and Parzen Windows 29.9 Experiments 29.9.1 Simulated Data Experiment 29.9.2 Real Data Experiment 29.9.2.1 Methods 29.9.2.2 Data Sets 29.9.2.3 Experimental Results 29.10 Summary References 30 Multiscale DDDAS Framework for Damage Prediction in Aerospace Composite Structures 30.1 Introduction 30.2 The Multiscale DDDAS Framework 30.3 Computational Structural Model 30.3.1 Progressive Damage Model 30.4 Fatigue Damage Simulation of a Full-Scale CX-100 Wind Turbine Blade Driven by Test Data 30.4.1 Blade Structure and Its IGA Model 30.4.2 Blade Fatigue-Test Setup and Sensor Layout 30.4.3 Blade Fatigue Simulation Driven by Test Data 30.5 Numerical Simulation of the Orion UAV 30.5.1 Parametric UAV Model 30.5.2 Landing Simulation 30.6 Conclusions References 31 A Dynamic Data-driven Stochastic State-Awareness Framework for the Next Generation of Bio-inspired Fly-by-feel Aerospace Vehicles 31.1 Introduction 31.2 Bio-inspired Sensor Networks and Wing Integration 31.2.1 The Composite Wing 31.3 The Wind Tunnel Experimental Process 31.3.1 The Wind Tunnel 31.3.2 The Experiments 31.4 Stochastic Global Identification Under Multiple Flight States 31.4.1 Baseline Modeling Under a Single Flight State 31.4.2 Global Modeling Under Multiple Flight States 31.5 Results 31.5.1 Numerical Simulations 31.5.2 Non-parametric Analysis 31.5.3 Baseline Parametric Modeling 31.5.4 Global Modeling Under Multiple Flight States 31.6 Concluding Remarks References 32 The Future of DDDAS 32.1 *-5pt 32.2 DDDAS Has Universal Appeal 32.2.1 Paradigm for Theory-Data Symbiosis 32.2.2 Mitigates the Curse of Dimensionality 32.2.3 A Prediction and Discovery Instrument 32.3 Emerging Opportunities 32.3.1 Applications Systems 32.3.2 Instrumentation 32.3.3 Modeling and Simulation Methodology 32.3.4 Systems Software Computation 32.4 Example: Hurricane Prediction 32.5 Conclusions References Index The Handbook Of Dynamic Data Driven Applications Systems Establishes An Authoritative Reference Of Dddas, Pioneered By Dr. Darema And The Co-authors For Researchers And Practitioners Developing Dddas Technologies. Beginning With General Concepts And History Of The Paradigm, The Text Provides 32 Chapters By Leading Experts In Ten Application Areas To Enable An Accurate Understanding, Analysis, And Control Of Complex Systems; Be They Natural, Engineered, Or Societal: Earth And Space Data Assimilation Aircraft Systems Processing Structures Health Monitoring Biological Data Assessment Object And Activity Tracking Embedded Control And Coordination Energy-aware Optimization Image And Video Computing Security And Policy Coding Systems Design The Authors Explain How Dddas Unifies The Computational And Instrumentation Aspects Of An Application System, Extends The Notion Of Smart Computing To Span From The High-end To The Real-time Data Acquisition And Control, And Manages Big Data Exploitation With High-dimensional Model Coordination.