Handbook of Dynamic Data Driven Applications Systems

lt;p>Dr. Erik P. Blasch is a Program Officer with the Air Force Office of Scientific Research. His focus areas are in multi-domain (space, air, ground) data fusion, target tracking, pattern recognition, and robotics. He has authored 750+ scientific papers, 22 patents, 30 tutorials, and 5 books. Recognitions include the Military Sensing Society Mignogna leadership in data fusion award, IEEE Aerospace and Electronics Systems Society Mimno best magazine paper award, IEEE Russ bioengineering award, and founding member of the International Society of Information Fusion (ISIF). Previous appointments include Adjunct Associate professor at Wright State University, Exchange scientist at Defense Research and Development Canada, and officer in the Air Force Research Laboratory. Dr. Blasch is an Associate Fellow of AIAA, Fellow of SPIE, and Fellow of IEEE.

Dr. Frederica Darema a member of the Senior Executive Service of the U.S. Air Force (retired), was the Director of Air Force Office of Scientific Research, Arlington, Virginia. She guided the management of the entire basic research investment for the Air Force. Dr. Darema led a staff of 200 scientists, engineers and administrators in Arlington, Virginia, and foreign technology offices in London, Tokyo and Santiago, Chile. 

 Dr. Sai Ravela directs the Earth Signals and Systems Group with research interests in Dynamic Data Driven Observing Systems at the Massachusetts Institute of Technology (MIT). He has made key contributions to Dynamic Data Driven cooperative autonomous observation of fluids, atmosphere, wildlife, retail intelligence, and micro-positioning radar. He has pioneered DDDAS concepts, and organized the first three DDDAS conferences that form the basis of this book. He has over 100 publications and patents, is the co-founder of Windrisktech LLC and E5 Aerospace LLC, and is a recipient of the MIT Infinite Kilometer award for exceptional research and outstanding mentorship.

 Dr. Alex J. Aved is a Senior Researcher with the Air Force Research Laboratory, Information Directorate, Rome, NY, USA. His research interests include multimedia databases, stream processing (via CPU, GPU, or coprocessor) and dynamically executing models with feedback loops incorporating measurement and error data to improve the accuracy of the model. He has published over 50 papers and given numerous invited lectures. Previously he was a programmer at the University of Central Florida and database administrator and programmer at Anderson University.

1 Introduction to Dynamic Data Driven Applications Systems.- 2 Tractable Non-Gaussian Representation in Dynamic Data Driven Coherent Fluid Mapping.- 3 Dynamic Data-Driven Adaptive Observations in Data Assimilation for Multi-scale Systems.- 4 Dynamic Data-Driven Uncertainty Quantification via Polynomial Chaos for Space Situational Awareness.- 5 Towards Learning Spatio-Temporal Data Stream Relationships for Failure Detection in Avionics.- 6 Markov Modeling of Time Series via Spectral Analysis for Detection of Combustion Instabilities.- 7 Dynamic Space-Time Model for Syndromic Surveillance with Particle Filters and Dirichlet Process.- 8 A Computational Steering Framework for Large-Scale Composite Structures.- 9 Development of Intelligent and Predictive Self-Healing Composite Structures using Dynamic Data-Driven Applications Systems.- 10 Dynamic Data-Driven Approach for Unmanned Aircraft Systems aero-elastic response analysis.- 11 Transforming Wildfire Detection and Prediction using New and Underused Sensor and Data Sources Integrated with Modeling.- 12 Dynamic Data Driven Application Systems for Identification of Biomarkers in DNA Methylation.- 13 Photometric Steropsis for 3D Reconstruction of Space Objects.- 14 Aided Optimal Search: Data-Driven Target Pursuit from On-Demand Delayed Binary Observations.- 15 Optimization of Multi-Target Tracking within a Sensor Network via Information Guided Clustering.- 16 Data-Driven Prediction of Confidence for EVAR in Time-varying Datasets.- 17 DDDAS for Attack Detection and Isolation of Control Systems.- 18 Approximate Local Utility Design for Potential Game Approach to Cooperative Sensor Network Planning.- 19 Dynamic Sensor-Actor Interactions for Path-Planning in a Threat Field.- 20 Energy-Aware Dynamic Data-Driven Distributed Traffic Simulation for Energy and Emissions Reduction.- 21 A Dynamic Data-Driven Optimization Framework for Demand Side Management in Microgrids.- 22 Dynamic Data Driven Partitioning of Smart Grid Using Learning Methods.- 23 Design of a Dynamic Data-Driven System for Multispectral Video Processing.- 24 Light Field Image Compression.- 25 On Compression of Machine-derived Context Sets for Fusion of Multi-model Sensor Data.- 26 Simulation-based Optimization as a Service for Dynamic Data-driven Applications Systems.- 27 Privacy and Security Issues in DDDAS Systems.- 28 Dynamic Data Driven Application Systems (DDDAS) for Multimedia Content Analysis.- 29 Parzen Windows: Simplest Regularization Algorithm.- 30 Multiscale DDDAS Framework for Damage Prediction in Aerospace Composite Structures.- 31 A Dynamic Data-Driven Stochastic State-awareness Framework for the Next Generation of Bio-inspired Fly-by-feel Aerospace Vehicles.- DDDAS: The Way Forward.      

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